hypothesis space search in machine learning ppt

• Data Science
• Data Analysis
• Data Visualization
• Machine Learning
• Deep Learning
• Computer Vision
• Artificial Intelligence
• AI ML DS Interview Series
• AI ML DS Projects series
• Data Engineering
• Web Scrapping

Hypothesis in Machine Learning

The concept of a hypothesis is fundamental in Machine Learning and data science endeavours. In the realm of machine learning, a hypothesis serves as an initial assumption made by data scientists and ML professionals when attempting to address a problem. Machine learning involves conducting experiments based on past experiences, and these hypotheses are crucial in formulating potential solutions.

It’s important to note that in machine learning discussions, the terms “hypothesis” and “model” are sometimes used interchangeably. However, a hypothesis represents an assumption, while a model is a mathematical representation employed to test that hypothesis. This section on “Hypothesis in Machine Learning” explores key aspects related to hypotheses in machine learning and their significance.

Table of Content

How does a Hypothesis work?

Hypothesis space and representation in machine learning, hypothesis in statistics, faqs on hypothesis in machine learning.

A hypothesis in machine learning is the model’s presumption regarding the connection between the input features and the result. It is an illustration of the mapping function that the algorithm is attempting to discover using the training set. To minimize the discrepancy between the expected and actual outputs, the learning process involves modifying the weights that parameterize the hypothesis. The objective is to optimize the model’s parameters to achieve the best predictive performance on new, unseen data, and a cost function is used to assess the hypothesis’ accuracy.

In most supervised machine learning algorithms, our main goal is to find a possible hypothesis from the hypothesis space that could map out the inputs to the proper outputs. The following figure shows the common method to find out the possible hypothesis from the Hypothesis space:

Hypothesis Space (H)

Hypothesis space is the set of all the possible legal hypothesis. This is the set from which the machine learning algorithm would determine the best possible (only one) which would best describe the target function or the outputs.

Hypothesis (h)

A hypothesis is a function that best describes the target in supervised machine learning. The hypothesis that an algorithm would come up depends upon the data and also depends upon the restrictions and bias that we have imposed on the data.

The Hypothesis can be calculated as:

[Tex]y = mx + b [/Tex]

• m = slope of the lines
• b = intercept

To better understand the Hypothesis Space and Hypothesis consider the following coordinate that shows the distribution of some data:

Say suppose we have test data for which we have to determine the outputs or results. The test data is as shown below:

We can predict the outcomes by dividing the coordinate as shown below:

So the test data would yield the following result:

But note here that we could have divided the coordinate plane as:

The way in which the coordinate would be divided depends on the data, algorithm and constraints.

• All these legal possible ways in which we can divide the coordinate plane to predict the outcome of the test data composes of the Hypothesis Space.
• Each individual possible way is known as the hypothesis.

Hence, in this example the hypothesis space would be like:

The hypothesis space comprises all possible legal hypotheses that a machine learning algorithm can consider. Hypotheses are formulated based on various algorithms and techniques, including linear regression, decision trees, and neural networks. These hypotheses capture the mapping function transforming input data into predictions.

Hypothesis Formulation and Representation in Machine Learning

Hypotheses in machine learning are formulated based on various algorithms and techniques, each with its representation. For example:

• Linear Regression : [Tex] h(X) = \theta_0 + \theta_1 X_1 + \theta_2 X_2 + … + \theta_n X_n[/Tex]
• Decision Trees : [Tex]h(X) = \text{Tree}(X)[/Tex]
• Neural Networks : [Tex]h(X) = \text{NN}(X)[/Tex]

In the case of complex models like neural networks, the hypothesis may involve multiple layers of interconnected nodes, each performing a specific computation.

Hypothesis Evaluation:

The process of machine learning involves not only formulating hypotheses but also evaluating their performance. This evaluation is typically done using a loss function or an evaluation metric that quantifies the disparity between predicted outputs and ground truth labels. Common evaluation metrics include mean squared error (MSE), accuracy, precision, recall, F1-score, and others. By comparing the predictions of the hypothesis with the actual outcomes on a validation or test dataset, one can assess the effectiveness of the model.

Hypothesis Testing and Generalization:

Once a hypothesis is formulated and evaluated, the next step is to test its generalization capabilities. Generalization refers to the ability of a model to make accurate predictions on unseen data. A hypothesis that performs well on the training dataset but fails to generalize to new instances is said to suffer from overfitting. Conversely, a hypothesis that generalizes well to unseen data is deemed robust and reliable.

The process of hypothesis formulation, evaluation, testing, and generalization is often iterative in nature. It involves refining the hypothesis based on insights gained from model performance, feature importance, and domain knowledge. Techniques such as hyperparameter tuning, feature engineering, and model selection play a crucial role in this iterative refinement process.

In statistics , a hypothesis refers to a statement or assumption about a population parameter. It is a proposition or educated guess that helps guide statistical analyses. There are two types of hypotheses: the null hypothesis (H0) and the alternative hypothesis (H1 or Ha).

• Null Hypothesis(H 0 ): This hypothesis suggests that there is no significant difference or effect, and any observed results are due to chance. It often represents the status quo or a baseline assumption.
• Aternative Hypothesis(H 1 or H a ): This hypothesis contradicts the null hypothesis, proposing that there is a significant difference or effect in the population. It is what researchers aim to support with evidence.

Q. How does the training process use the hypothesis?

The learning algorithm uses the hypothesis as a guide to minimise the discrepancy between expected and actual outputs by adjusting its parameters during training.

Q. How is the hypothesis’s accuracy assessed?

Usually, a cost function that calculates the difference between expected and actual values is used to assess accuracy. Optimising the model to reduce this expense is the aim.

Q. What is Hypothesis testing?

Hypothesis testing is a statistical method for determining whether or not a hypothesis is correct. The hypothesis can be about two variables in a dataset, about an association between two groups, or about a situation.

Q. What distinguishes the null hypothesis from the alternative hypothesis in machine learning experiments?

The null hypothesis (H0) assumes no significant effect, while the alternative hypothesis (H1 or Ha) contradicts H0, suggesting a meaningful impact. Statistical testing is employed to decide between these hypotheses.

Please Login to comment...

Similar reads.

• Best External Hard Drives for Mac in 2024: Top Picks for MacBook Pro, MacBook Air & More
• How to Watch NFL Games Live Streams Free
• OpenAI o1 AI Model Launched: Explore o1-Preview, o1-Mini, Pricing & Comparison
• How to Merge Cells in Google Sheets: Step by Step Guide
• #geekstreak2024 – 21 Days POTD Challenge Powered By Deutsche Bank

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

ID3 Algorithm and Hypothesis space in Decision Tree Learning

The collection of potential decision trees is the hypothesis space searched by ID3. ID3 searches this hypothesis space in a hill-climbing fashion, starting with the empty tree and moving on to increasingly detailed hypotheses in pursuit of a decision tree that properly classifies the training data.

In this blog, we’ll have a look at the Hypothesis space in Decision Trees and the ID3 Algorithm. 

ID3 Algorithm: 

The ID3 algorithm (Iterative Dichotomiser 3) is a classification technique that uses a greedy approach to create a decision tree by picking the optimal attribute that delivers the most Information Gain (IG) or the lowest Entropy (H).

What is Information Gain and Entropy?  

Information gain: .

The assessment of changes in entropy after segmenting a dataset based on a characteristic is known as information gain.

It establishes how much information a feature provides about a class.

We divided the node and built the decision tree based on the value of information gained.

The greatest information gain node/attribute is split first in a decision tree method, which always strives to maximize the value of information gain. 

The formula for Information Gain: 

Entropy is a metric for determining the degree of impurity in a particular property. It denotes the unpredictability of data. The following formula may be used to compute entropy:

S stands for “total number of samples.”

P(yes) denotes the likelihood of a yes answer.

P(no) denotes the likelihood of a negative outcome.

• Calculate the dataset’s entropy.
• For each feature/attribute.

Determine the entropy for each of the category values.

Calculate the feature’s information gain.

• Find the feature that provides the most information.
• Repeat it till we get the tree we want.

Characteristics of ID3: 

• ID3 takes a greedy approach, which means it might become caught in local optimums and hence cannot guarantee an optimal result.
• ID3 has the potential to overfit the training data (to avoid overfitting, smaller decision trees should be preferred over larger ones).
• This method creates tiny trees most of the time, however, it does not always yield the shortest tree feasible.
• On continuous data, ID3 is not easy to use (if the values of any given attribute are continuous, then there are many more places to split the data on this attribute, and searching for the best value to split by takes a lot of time).

Over Fitting:  

Good generalization is the desired property in our decision trees (and, indeed, in all classification problems), as we noted before. 

This implies we want the model fit on the labeled training data to generate predictions that are as accurate as they are on new, unseen observations.

Capabilities and Limitations of ID3:

• In relation to the given characteristics, ID3’s hypothesis space for all decision trees is a full set of finite discrete-valued functions.
• As it searches across the space of decision trees, ID3 keeps just one current hypothesis. This differs from the prior version space candidate Elimination approach, which keeps the set of all hypotheses compatible with the training instances provided.
• ID3 loses the capabilities that come with explicitly describing all consistent hypotheses by identifying only one hypothesis. It is unable to establish how many different decision trees are compatible with the supplied training data.
• One benefit of incorporating all of the instances’ statistical features (e.g., information gain) is that the final search is less vulnerable to faults in individual training examples.
• By altering its termination criterion to allow hypotheses that inadequately match the training data, ID3 may simply be modified to handle noisy training data.
• In its purest form, ID3 does not go backward in its search. It never goes back to evaluate a choice after it has chosen an attribute to test at a specific level in the tree. As a result, it is vulnerable to the standard dangers of hill-climbing search without backtracking, resulting in local optimum but not globally optimal solutions.
• At each stage of the search, ID3 uses all training instances to make statistically based judgments on how to refine its current hypothesis. This is in contrast to approaches that make incremental judgments based on individual training instances (e.g., FIND-S or CANDIDATE-ELIMINATION ).

Hypothesis Space Search by ID3: 

• ID3 climbs the hill of knowledge acquisition by searching the space of feasible decision trees.
• It looks for all finite discrete-valued functions in the whole space. Every function is represented by at least one tree.
• It only holds one theory (unlike Candidate-Elimination). It is unable to inform us how many more feasible options exist.
• It’s possible to get stranded in local optima.
• At each phase, all training examples are used. Errors have a lower impact on the outcome.

• My presentations

Auth with social network:

Download presentation

We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!

Presentation is loading. Please wait.

Decision Tree Learning

Published by Modified over 9 years ago

Similar presentations

Presentation on theme: "Decision Tree Learning"— Presentation transcript:

Concept Learning and the General-to-Specific Ordering

1 Machine Learning: Lecture 3 Decision Tree Learning (Based on Chapter 3 of Mitchell T.., Machine Learning, 1997)

Decision Trees Decision tree representation ID3 learning algorithm

CPSC 502, Lecture 15Slide 1 Introduction to Artificial Intelligence (AI) Computer Science cpsc502, Lecture 15 Nov, 1, 2011 Slide credit: C. Conati, S.

1er. Escuela Red ProTIC - Tandil, de Abril, Decision Tree Learning 3.1 Introduction –Method for approximation of discrete-valued target functions.

Decision Tree Approach in Data Mining

ICS320-Foundations of Adaptive and Learning Systems

Classification Techniques: Decision Tree Learning

Decision Tree Learning 主講人：虞台文 大同大學資工所 智慧型多媒體研究室.

Decision Trees IDHairHeightWeightLotionResult SarahBlondeAverageLightNoSunburn DanaBlondeTallAverageYesnone AlexBrownTallAverageYesNone AnnieBlondeShortAverageNoSunburn.

Part 7.3 Decision Trees Decision tree representation ID3 learning algorithm Entropy, information gain Overfitting.

Decision Tree Algorithm

CS 590M Fall 2001: Security Issues in Data Mining Lecture 4: ID3.

Decision tree LING 572 Fei Xia 1/10/06. Outline Basic concepts Main issues Advanced topics.

Decision Tree Learning Learning Decision Trees (Mitchell 1997, Russell & Norvig 2003) –Decision tree induction is a simple but powerful learning paradigm.

Induction of Decision Trees

Evaluating Hypotheses

Data Mining with Decision Trees Lutz Hamel Dept. of Computer Science and Statistics University of Rhode Island.

1 Interacting with Data Materials from a Course in Princeton University -- Hu Yan.

About project

© 2024 SlidePlayer.com Inc. All rights reserved.

Data Science Introduction

• What Is Data Science? A Beginner's Guide To Data Science
• Data Science Tutorial – Learn Data Science from Scratch!
• What are the Best Books for Data Science?
• Top 15 Hot Artificial Intelligence Technologies
• Top 8 Data Science Tools Everyone Should Know
• Top 10 Data Analytics Tools You Need To Know In 2024
• 5 Data Science Projects – Data Science Projects For Practice
• Top 10 Data Science Applications with Real Life Examples in 2024
• Who is a Data Scientist?
• SQL For Data Science: One stop Solution for Beginners

Statistical Inference

• All You Need To Know About Statistics And Probability
• A Complete Guide To Math And Statistics For Data Science
• Introduction To Markov Chains With Examples – Markov Chains With Python

What is Fuzzy Logic in AI and What are its Applications?

• How To Implement Bayesian Networks In Python? – Bayesian Networks Explained With Examples
• All You Need To Know About Principal Component Analysis (PCA)
• Python for Data Science – How to Implement Python Libraries

Machine Learning

• What is Machine Learning? Machine Learning For Beginners
• Which is the Best Book for Machine Learning?
• Mathematics for Machine Learning: All You Need to Know
• Top 10 Machine Learning Frameworks You Need to Know
• Predicting the Outbreak of COVID-19 Pandemic using Machine Learning
• Introduction To Machine Learning: All You Need To Know About Machine Learning
• Machine Learning Tutorial for Beginners
• Top 10 Applications of Machine Learning in Daily Life
• Machine Learning Algorithms

How To Implement Find-S Algorithm In Machine Learning?

What is cross-validation in machine learning and how to implement it.

• All You Need To Know About The Breadth First Search Algorithm

Supervised Learning

• What is Supervised Learning and its different types?
• Linear Regression Algorithm from Scratch

How To Implement Linear Regression for Machine Learning?

• Introduction to Classification Algorithms
• How To Implement Classification In Machine Learning?
• Naive Bayes Classifier: Learning Naive Bayes with Python
• A Comprehensive Guide To Naive Bayes In R
• A Complete Guide On Decision Tree Algorithm
• Decision Tree: How To Create A Perfect Decision Tree?
• What is Overfitting In Machine Learning And How To Avoid It?

How To Use Regularization in Machine Learning?

Unsupervised learning.

• What is Unsupervised Learning and How does it Work?
• K-means Clustering Algorithm: Know How It Works
• KNN Algorithm: A Practical Implementation Of KNN Algorithm In R
• Implementing K-means Clustering on the Crime Dataset
• K-Nearest Neighbors Algorithm Using Python
• Apriori Algorithm : Know How to Find Frequent Itemsets
• What Are GANs? How and why you should use them!
• Q Learning: All you need to know about Reinforcement Learning

Miscellaneous

• Data Science vs Machine Learning - What's The Difference?
• AI vs Machine Learning vs Deep Learning
• Data Analyst vs Data Engineer vs Data Scientist: Salary, Skills, Responsibilities
• Data Science vs Big Data vs Data Analytics

Career Opportunities

• Data Science Career Opportunities: Your Guide To Unlocking Top Data Scientist Jobs
• Data Scientist Skills – What Does It Take To Become A Data Scientist?
• 10 Skills To Master For Becoming A Data Scientist
• Data Scientist Resume Sample – How To Build An Impressive Data Scientist Resume
• Data Scientist Salary – How Much Does A Data Scientist Earn?
• Machine Learning Engineer vs Data Scientist : Career Comparision
• How To Become A Machine Learning Engineer? – Learning Path

Interview Questions

• Top Machine Learning Interview Questions You Must Prepare In 2024
• Top Data Science Interview Questions For Budding Data Scientists In 2024
• 120+ Data Science Interview Questions And Answers for 2024

Artificial Intelligence

In Machine Learning , concept learning can be termed as “ a problem of searching through a predefined space of potential hypothesis for the hypothesis that best fits the training examples” – Tom Mitchell. In this article, we will go through one such concept learning algorithm known as the Find-S algorithm. If you want to go beyond this article and really want the level of expertise in you – you can get certified in Machine Learning Certification!

Machine Learning Full Course – Learn Machine Learning 10 Hours | Machine Learning Tutorial | Edureka

Machine Learning Course lets you master the application of AI with the expert guidance. It includes various algorithms with applications.

The following topics are discussed in this article.

What is Find-S Algorithm in Machine Learning?

• How Does it Work?

Limitations of Find-S Algorithm

Implementation of find-s algorithm.

In order to understand Find-S algorithm, you need to have a basic idea of the following concepts as well:

• Concept Learning
• General Hypothesis
• Specific Hypothesis

1. Concept Learning 

Let’s try to understand concept learning with a real-life example. Most of human learning is based on past instances or experiences. For example, we are able to identify any type of vehicle based on a certain set of features like make, model, etc., that are defined over a large set of features.

These special features differentiate the set of cars, trucks, etc from the larger set of vehicles. These features that define the set of cars, trucks, etc are known as concepts.

Similar to this, machines can also learn from concepts to identify whether an object belongs to a specific category or not. Any algorithm that supports concept learning requires the following:

• Training Data
• Target Concept
• Actual Data Objects

2. General Hypothesis

Hypothesis, in general, is an explanation for something. The general hypothesis basically states the general relationship between the major variables. For example, a general hypothesis for ordering food would be  I want a burger.

G = { ‘?’, ‘?’, ‘?’, …..’?’}

3. Specific Hypothesis

The specific hypothesis fills in all the important details about the variables given in the general hypothesis. The more specific details into the example given above would be  I want a cheeseburger with a chicken pepperoni filling with a lot of lettuce. 

S = {‘Φ’,’Φ’,’Φ’, ……,’Φ’}

Now ,let’s talk about the Find-S Algorithm in Machine Learning.

The Find-S algorithm follows the steps written below:

• Initialize ‘h’ to the most specific hypothesis.
• The Find-S algorithm only considers the positive examples and eliminates negative examples. For each positive example, the algorithm checks for each attribute in the example. If the attribute value is the same as the hypothesis value, the algorithm moves on without any changes. But if the attribute value is different than the hypothesis value, the algorithm changes it to ‘?’.

Now that we are done with the basic explanation of the Find-S algorithm, let us take a look at how it works.

How Does It Work?

• The process starts with initializing ‘h’ with the most specific hypothesis, generally, it is the first positive example in the data set.
• We check for each positive example. If the example is negative, we will move on to the next example but if it is a positive example we will consider it for the next step.
• We will check if each attribute in the example is equal to the hypothesis value.
• If the value matches, then no changes are made.
• If the value does not match, the value is changed to ‘?’.
• We do this until we reach the last positive example in the data set.

There are a few limitations of the Find-S algorithm listed down below:

• There is no way to determine if the hypothesis is consistent throughout the data.
• Inconsistent training sets can actually mislead the Find-S algorithm, since it ignores the negative examples.
• Find-S algorithm does not provide a backtracking technique to determine the best possible changes that could be done to improve the resulting hypothesis.

Top 10 Trending Technologies to Learn in 2024 | Edureka

Now that we are aware of the limitations of the Find-S algorithm, let us take a look at a practical implementation of the Find-S Algorithm.

To understand the implementation, let us try to implement it to a smaller data set with a bunch of examples to decide if a person wants to go for a walk.

The concept of this particular problem will be on what days does a person likes to go on walk.

Morning	Sunny	Warm	Yes	Mild	Strong	Yes
Evening	Rainy	Cold	No	Mild	Normal	No
Morning	Sunny	Moderate	Yes	Normal	Normal	Yes
Evening	Sunny	Cold	Yes	High	Strong	Yes

Looking at the data set, we have six attributes and a final attribute that defines the positive or negative example. In this case, yes is a positive example, which means the person will go for a walk.

So now, the general hypothesis is:

h 0 = {‘Morning’, ‘Sunny’, ‘Warm’, ‘Yes’, ‘Mild’, ‘Strong’}

This is our general hypothesis, and now we will consider each example one by one, but only the positive examples.

h 1 = {‘Morning’, ‘Sunny’, ‘?’, ‘Yes’, ‘?’, ‘?’}

h 2 = {‘?’, ‘Sunny’, ‘?’, ‘Yes’, ‘?’, ‘?’}

We replaced all the different values in the general hypothesis to get a resultant hypothesis. Now that we know how the Find-S algorithm works, let us take a look at an implementation using Python .

Let’s try to implement the above example using Python . The code to implement the Find-S algorithm using the above data is given below.

This brings us to the end of this article where we have learned the Find-S Algorithm in Mach ine Learning with its implementation and use case. I hope you are clear with all that has been shared with you in this tutorial.

With immense applications and easier implementations of Python with data science, there has been a significant increase in the number of jobs created for data science every year. Enroll for Edureka’s Data Science with Python and get hands-on experience with real-time industry projects along with 24×7 support, which will set you on the path of becoming a successful Data Scientist,

We are here to help you with every step on your journey and come up with a curriculum that is designed for students and professionals who want to be a   Machine Learning Engineer . The course is designed to give you a head start into Python programming and train you for both core and advanced Python concepts along with various   Machine learning Algorithms   like  SVM ,  Decision Tree , etc.

If you come across any questions, feel free to ask all your questions in the comments section of “Find-S Algorithm In Machine Learning” and our team will be glad to answer.

","eventStatus":"https://schema.org/EventScheduled","image":"https://d1jnx9ba8s6j9r.cloudfront.net/imgver.0000000000/img/co_img_2263_1678870066.png","startDate":"2024-09-21T07:00:00+0000","endDate":"2024-11-03T10:00:00+0000","Duration":"R8/P7DT4H","inLanguage":"en-US","url":"https://www.edureka.co/masters-program/machine-learning-engineer-training?utm_source=blogbatch&utm_campaign=batch_details","eventAttendanceMode":"https://schema.org/OnlineEventAttendanceMode","location":[{"@type":"VirtualLocation","url":"https://www.edureka.co/masters-program/machine-learning-engineer-training?utm_source=blogbatch&utm_campaign=batch_details"}],"organizer":{"@type":"Thing","name":"Edureka","url":"https://www.edureka.co"},"performer":"Edureka","offers":{"@type":"AggregateOffer","url":"https://www.edureka.co/masters-program/machine-learning-engineer-training?utm_source=blogbatch&utm_campaign=batch_details","availability":"InStock","price":"1499","priceCurrency":"USD","lowprice":"1499","highprice":"1499","validFrom":"2024-09-18T00:00:00+0000"}}

Course Name	Date	Details
	Class Starts on 21st September,2024 21st September SAT&SUN (Weekend Batch)

Recommended videos for you

Introduction to mahout, recommended blogs for you, what is ai ethics and how to implement it , ai in supply chain: understand the benefits and challenges , ai for startups: opportunities, challenges, and best practices, what is artificial intelligence (ai) on microsoft azure, ai in customer services: a complete guide, what is chatgpt everything you need to know about chat gpt, what is ai in cyber security uses, benefits, tools, how to become an artificial intelligence engineer a roadmap to the future, best chatgpt alternatives you must try, how to become a machine learning engineer, generative ai tutorial for beginners: a comprehensive guide, tabnine vs. github copilot: a comprehensive comparison, ai in banking: 5 applications of artificial intelligence in banking, what is responsible ai – a complete guide, popular generative ai tools you must know, join the discussion cancel reply, trending courses in artificial intelligence, human-computer interaction (hci) for ai syste ....

• 2k Enrolled Learners
• Weekend/Weekday

Artificial Intelligence Certification Course

• 16k Enrolled Learners

ChatGPT Training Course: Beginners to Advance ...

• 15k Enrolled Learners

Prompt Engineering Course

• 5k Enrolled Learners

Generative AI in Business: University of Camb ...

• 1k Enrolled Learners

MLOps Certification Course Online

• 6k Enrolled Learners

Large Language Models (LLMs) Certification Co ...

• 3k Enrolled Learners

Reinforcement Learning

Graphical models certification training, generative ai in hr certification course, browse categories, subscribe to our newsletter, and get personalized recommendations..

Already have an account? Sign in .

20,00,000 learners love us! Get personalised resources in your inbox.

At least 1 upper-case and 1 lower-case letter

Minimum 8 characters and Maximum 50 characters

We have recieved your contact details.

You will recieve an email from us shortly.

← ^ →

Genetic Algorithms

Hypothesis Space Search

• In Backpropagation we moved smoothly from one hypothesis to a nearby one. In GA search we can take longer jumps and maintain a population of hypotheses.
• Q: How do we characterize the evolution of a population?
• A Schema is a string containing 0, 1, or *. The * means “don't care.” e.g. Schema: 10**0* Instances: 101101, 100100, ...
• We can then characterize the population by the number of instances representing each possible schema.
• $m(s,t)$ = number of instances of schema $s$ in population at time $t$.

José M. Vidal .

What’s a Hypothesis Space?

Last updated: March 18, 2024

• Math and Logic

Baeldung Pro comes with both absolutely No-Ads as well as finally with Dark Mode , for a clean learning experience:

>> Explore a clean Baeldung

Once the early-adopter seats are all used, the price will go up and stay at $33/year.

1. Introduction

Machine-learning algorithms come with implicit or explicit assumptions about the actual patterns in the data. Mathematically, this means that each algorithm can learn a specific family of models, and that family goes by the name of the hypothesis space.

In this tutorial, we’ll talk about hypothesis spaces and how to choose the right one for the data at hand.

2. Hypothesis Spaces

Let’s say that we have a binary classification task and that the data are two-dimensional. Our goal is to find a model that classifies objects as positive or negative. Applying Logistic Regression , we can get the models of the form:

which estimate the probability that the object at hand is positive.

2.1. Hypotheses and Assumptions

The underlying assumption of hypotheses ( 1 ) is that the boundary separating the positive from negative objects is a straight line. So, every hypothesis from this space corresponds to a straight line in a 2D plane. For instance:

2.2. Regression

3. expressivity of a hypothesis space.

We could informally say that one hypothesis space is more expressive than another if its hypotheses are more diverse and complex.

We may underfit the data if our algorithm’s hypothesis space isn’t expressive enough. For instance, linear hypotheses aren’t particularly good options if the actual data are extremely non-linear:

So, training an algorithm that has a very expressive space increases the chance of completely capturing the patterns in the data. However, it also increases the risk of overfitting. For instance, a space containing the hypotheses of the form:

would start modelling the noise, which we see from its decision boundary:

Such models would generalize poorly to unseen data.

3.1. Expressivity vs. Interpretability

Additionally, even if a complex hypothesis has a good generalization capability, it may be unusable in practice because it’s too complicated to understand or compute. What’s more, intricated hypotheses offer limited insight into the real-world process that generated the data. For example, a quadratic model:

4. How to Choose the Hypothesis Space?

We need to find the right balance between expressivity and simplicity. Unfortunately, that’s easier said than done. Most of the time, we need to rely on our intuition about the data.

So, we should start by exploring the dataset, using visualizations as much as possible. For instance, we can conclude that a straight line isn’t likely to be an adequate boundary for the above classification data. However, a high-order curve would probably be too complex even though it might split the dataset into two classes without an error.

A second-degree curve might be the compromise we seek, but we aren’t sure. So, we start with the space of quadratic hypotheses:

We get a model whose decision boundary appears to be a good fit even though it misclassifies some objects:

Since we’re satisfied with the model, we can stop here. If that hadn’t been the case, we could have tried a space of cubic models. The idea would be to iteratively try incrementally complex families until finding a model that both performs well and is easy to understand.

4. Conclusion

In this article, we talked about hypotheses spaces in machine learning. An algorithm’s hypothesis space contains all the models it can learn from any dataset.

The algorithms with too expressive spaces can generalize poorly to unseen data and be too complex to understand, whereas those with overly simple hypotheses may underfit the data. So, when applying machine-learning algorithms in practice, we need to find the right balance between expressivity and simplicity.

Machine Learning

• Machine Learning Tutorial
• Machine Learning Applications
• Life cycle of Machine Learning
• Install Anaconda & Python
• AI vs Machine Learning
• How to Get Datasets
• Data Preprocessing
• Supervised Machine Learning
• Unsupervised Machine Learning
• Supervised vs Unsupervised Learning

Supervised Learning

• Regression Analysis
• Linear Regression
• Simple Linear Regression
• Multiple Linear Regression
• Backward Elimination
• Polynomial Regression

Classification

• Classification Algorithm
• Logistic Regression
• K-NN Algorithm
• Support Vector Machine Algorithm
• Na�ve Bayes Classifier

Miscellaneous

• Classification vs Regression
• Linear Regression vs Logistic Regression
• Decision Tree Classification Algorithm
• Random Forest Algorithm
• Clustering in Machine Learning
• Hierarchical Clustering in Machine Learning
• K-Means Clustering Algorithm
• Apriori Algorithm in Machine Learning
• Association Rule Learning
• Confusion Matrix
• Cross-Validation
• Data Science vs Machine Learning
• Machine Learning vs Deep Learning
• Dimensionality Reduction Technique
• Machine Learning Algorithms
• Overfitting & Underfitting
• Principal Component Analysis
• What is P-Value
• Regularization in Machine Learning
• Examples of Machine Learning
• Semi-Supervised Learning
• Essential Mathematics for Machine Learning
• Overfitting in Machine Learning
• Types of Encoding Techniques
• Feature Selection Techniques in Machine Learning
• Bias and Variance in Machine Learning
• Machine Learning Tools
• Prerequisites for Machine Learning
• Gradient Descent in Machine Learning
• Machine Learning Experts Salary in India
• Machine Learning Models
• Machine Learning Books
• Linear Algebra for Machine learning
• Types of Machine Learning
• Feature Engineering for Machine Learning
• Top 10 Machine Learning Courses in 2021
• Epoch in Machine Learning
• Machine Learning with Anomaly Detection
• What is Epoch
• Cost Function in Machine Learning
• Bayes Theorem in Machine learning
• Perceptron in Machine Learning
• Entropy in Machine Learning
• Issues in Machine Learning
• Precision and Recall in Machine Learning
• Genetic Algorithm in Machine Learning
• Normalization in Machine Learning
• Adversarial Machine Learning
• Basic Concepts in Machine Learning
• Machine Learning Techniques
• Demystifying Machine Learning
• Challenges of Machine Learning
• Model Parameter vs Hyperparameter
• Hyperparameters in Machine Learning
• Importance of Machine Learning
• Machine Learning and Cloud Computing
• Anti-Money Laundering using Machine Learning
• Data Science Vs. Machine Learning Vs. Big Data
• Popular Machine Learning Platforms
• Deep learning vs. Machine learning vs. Artificial Intelligence
• Machine Learning Application in Defense/Military
• Machine Learning Applications in Media
• How can Machine Learning be used with Blockchain
• Prerequisites to Learn Artificial Intelligence and Machine Learning
• List of Machine Learning Companies in India
• Mathematics Courses for Machine Learning
• Probability and Statistics Books for Machine Learning
• Risks of Machine Learning
• Best Laptops for Machine Learning
• Machine Learning in Finance
• Lead Generation using Machine Learning
• Machine Learning and Data Science Certification
• What is Big Data and Machine Learning
• How to Save a Machine Learning Model
• Machine Learning Model with Teachable Machine
• Data Structure for Machine Learning
• Hypothesis in Machine Learning
• Gaussian Discriminant Analysis
• How Machine Learning is used by Famous Companies
• Introduction to Transfer Learning in ML
• LDA in Machine Learning
• Stacking in Machine Learning
• CNB Algorithm
• Deploy a Machine Learning Model using Streamlit Library
• Different Types of Methods for Clustering Algorithms in ML
• EM Algorithm in Machine Learning
• Machine Learning Pipeline
• Exploitation and Exploration in Machine Learning
• Machine Learning for Trading
• Data Augmentation: A Tactic to Improve the Performance of ML
• Difference Between Coding in Data Science and Machine Learning
• Data Labelling in Machine Learning
• Impact of Deep Learning on Personalization
• Major Business Applications of Convolutional Neural Network
• Mini Batch K-means clustering algorithm
• What is Multilevel Modelling
• GBM in Machine Learning
• Back Propagation through time - RNN
• Data Preparation in Machine Learning
• Predictive Maintenance Using Machine Learning
• NLP Analysis of Restaurant Reviews
• What are LSTM Networks
• Performance Metrics in Machine Learning
• Optimization using Hopfield Network
• Data Leakage in Machine Learning
• Generative Adversarial Network
• Machine Learning for Data Management
• Tensor Processing Units
• Train and Test datasets in Machine Learning
• How to Start with Machine Learning
• AUC-ROC Curve in Machine Learning
• Targeted Advertising using Machine Learning
• Top 10 Machine Learning Projects for Beginners using Python
• What is Human-in-the-Loop Machine Learning
• What is MLOps
• K-Medoids clustering-Theoretical Explanation
• Machine Learning Or Software Development: Which is Better
• How does Machine Learning Work
• How to learn Machine Learning from Scratch
• Is Machine Learning Hard
• Face Recognition in Machine Learning
• Product Recommendation Machine Learning
• Designing a Learning System in Machine Learning
• Recommendation System - Machine Learning
• Customer Segmentation Using Machine Learning
• Detecting Phishing Websites using Machine Learning
• Hidden Markov Model in Machine Learning
• Sales Prediction Using Machine Learning
• Crop Yield Prediction Using Machine Learning
• Data Visualization in Machine Learning
• ELM in Machine Learning
• Probabilistic Model in Machine Learning
• Survival Analysis Using Machine Learning
• Traffic Prediction Using Machine Learning
• t-SNE in Machine Learning
• BERT Language Model
• Federated Learning in Machine Learning
• Deep Parametric Continuous Convolutional Neural Network
• Depth-wise Separable Convolutional Neural Networks
• Need for Data Structures and Algorithms for Deep Learning and Machine Learning
• Geometric Model in Machine Learning
• Machine Learning Prediction
• Scalable Machine Learning
• Credit Score Prediction using Machine Learning
• Extrapolation in Machine Learning
• Image Forgery Detection Using Machine Learning
• Insurance Fraud Detection -Machine Learning
• NPS in Machine Learning
• Sequence Classification- Machine Learning
• EfficientNet: A Breakthrough in Machine Learning Model Architecture
• focl algorithm in Machine Learning
• Gini Index in Machine Learning
• Rainfall Prediction using ML
• Major Kernel Functions in Support Vector Machine
• Bagging Machine Learning
• BERT Applications
• Xtreme: MultiLingual Neural Network
• History of Machine Learning
• Multimodal Transformer Models
• Pruning in Machine Learning
• ResNet: Residual Network
• Gold Price Prediction using Machine Learning
• Dog Breed Classification using Transfer Learning
• Cataract Detection Using Machine Learning
• Placement Prediction Using Machine Learning
• Stock Market prediction using Machine Learning
• How to Check the Accuracy of your Machine Learning Model
• Interpretability and Explainability: Transformer Models
• Pattern Recognition in Machine Learning
• Zillow Home Value (Zestimate) Prediction in ML
• Fake News Detection Using Machine Learning
• Genetic Programming VS Machine Learning
• IPL Prediction Using Machine Learning
• Document Classification Using Machine Learning
• Heart Disease Prediction Using Machine Learning
• OCR with Machine Learning
• Air Pollution Prediction Using Machine Learning
• Customer Churn Prediction Using Machine Learning
• Earthquake Prediction Using Machine Learning
• Factor Analysis in Machine Learning
• Locally Weighted Linear Regression
• Machine Learning in Restaurant Industry
• Machine Learning Methods for Data-Driven Turbulence Modeling
• Predicting Student Dropout Using Machine Learning
• Image Processing Using Machine Learning
• Machine Learning in Banking
• Machine Learning in Education
• Machine Learning in Healthcare
• Machine Learning in Robotics
• Cloud Computing for Machine Learning and Cognitive Applications
• Credit Card Approval Using Machine Learning
• Liver Disease Prediction Using Machine Learning
• Majority Voting Algorithm in Machine Learning
• Data Augmentation in Machine Learning
• Decision Tree Classifier in Machine Learning
• Machine Learning in Design
• Digit Recognition Using Machine Learning
• Electricity Consumption Prediction Using Machine Learning
• Data Analytics vs. Machine Learning
• Injury Prediction in Competitive Runners Using Machine Learning
• Protein Folding Using Machine Learning
• Sentiment Analysis Using Machine Learning
• Network Intrusion Detection System Using Machine Learning
• Titanic- Machine Learning From Disaster
• Adenovirus Disease Prediction for Child Healthcare Using Machine Learning
• RNN for Sequence Labelling
• CatBoost in Machine Learning
• Cloud Computing Future Trends
• Histogram of Oriented Gradients (HOG)
• Implementation of neural network from scratch using NumPy
• Introduction to SIFT( Scale Invariant Feature Transform)
• Introduction to SURF (Speeded-Up Robust Features)
• Kubernetes - load balancing service
• Kubernetes Resource Model (KRM) and How to Make Use of YAML
• Are Robots Self-Learning
• Variational Autoencoders
• What are the Security and Privacy Risks of VR and AR
• What is a Large Language Model (LLM)
• Privacy-preserving Machine Learning
• Continual Learning in Machine Learning
• Quantum Machine Learning (QML)
• Split Single Column into Multiple Columns in PySpark DataFrame
• Why should we use AutoML
• Evaluation Metrics for Object Detection and Recognition
• Mean Intersection over Union (mIoU) for image segmentation
• YOLOV5-Object-Tracker-In-Videos
• Predicting Salaries with Machine Learning
• Fine-tuning Large Language Models
• AutoML Workflow
• Build Chatbot Webapp with LangChain
• Building a Machine Learning Classification Model with PyCaret
• Continuous Bag of Words (CBOW) in NLP
• Deploying Scrapy Spider on ScrapingHub
• Dynamic Pricing Using Machine Learning
• How to Improve Neural Networks by Using Complex Numbers
• Introduction to Bayesian Deep Learning
• LiDAR: Light Detection and Ranging for 3D Reconstruction
• Meta-Learning in Machine Learning
• Object Recognition in Medical Imaging
• Region-level Evaluation Metrics for Image Segmentation
• Sarcasm Detection Using Neural Networks
• SARSA Reinforcement Learning
• Single Shot MultiBox Detector (SSD) using Neural Networking Approach
• Stepwise Predictive Analysis in Machine Learning
• Vision Transformers vs. Convolutional Neural Networks
• V-Net in Image Segmentation
• Forest Cover Type Prediction Using Machine Learning
• Ada Boost algorithm in Machine Learning
• Continuous Value Prediction
• Bayesian Regression
• Least Angle Regression
• Linear Models
• DNN Machine Learning
• Why do we need to learn Machine Learning
• Roles in Machine Learning
• Clustering Performance Evaluation
• Spectral Co-clustering
• 7 Best R Packages for Machine Learning
• Calculate Kurtosis
• Machine Learning for Data Analysis
• What are the benefits of 5G Technology for the Internet of Things
• What is the Role of Machine Learning in IoT
• Human Activity Recognition Using Machine Learning
• Components of GIS
• Attention Mechanism
• Backpropagation- Algorithm
• VGGNet-16 Architecture
• Independent Component Analysis
• Nonnegative Matrix Factorization
• Sparse Inverse Covariance
• Accuracy, Precision, Recall or F1
• L1 and L2 Regularization
• Maximum Likelihood Estimation
• Kernel Principal Component Analysis (KPCA)
• Latent Semantic Analysis
• Overview of outlier detection methods
• Robust Covariance Estimation
• Spectral Bi-Clustering
• Drift in Machine Learning
• Credit Card Fraud Detection Using Machine Learning
• KL-Divergence
• Transformers Architecture
• Novelty Detection with Local Outlier Factor
• Novelty Detection
• Introduction to Bayesian Linear Regression
• Firefly Algorithm
• Keras: Attention and Seq2Seq
• A Guide Towards a Successful Machine Learning Project
• ACF and PCF
• Bayesian Hyperparameter Optimization for Machine Learning
• Random Forest Hyperparameter tuning in python
• Simulated Annealing
• Top Benefits of Machine Learning in FinTech
• Weight Initialisation
• Density Estimation
• Overlay Network
• Micro, Macro Weighted Averages of F1 Score
• Assumptions of Linear Regression
• Evaluation Metrics for Clustering Algorithms
• Frog Leap Algorithm
• Isolation Forest
• McNemar Test
• Stochastic Optimization
• Geomagnetic Field Using Machine Learning
• Image Generation Using Machine Learning
• Confidence Intervals
• Facebook Prophet
• Understanding Optimization Algorithms in Machine Learning
• What Are Probabilistic Models in Machine Learning
• How to choose the best Linear Regression model
• How to Remove Non-Stationarity From Time Series
• AutoEncoders
• Cat Classification Using Machine Learning
• AIC and BIC
• Inception Model
• Architecture of Machine Learning
• Business Intelligence Vs Machine Learning
• Guide to Cluster Analysis: Applications, Best Practices
• Linear Regression using Gradient Descent
• Text Clustering with K-Means
• The Significance and Applications of Covariance Matrix
• Stationarity Tests in Time Series
• Graph Machine Learning
• Introduction to XGBoost Algorithm in Machine Learning
• Bahdanau Attention
• Greedy Layer Wise Pre-Training
• OneVsRestClassifier
• Best Program for Machine Learning
• Deep Boltzmann machines (DBMs) in machine learning
• Find Patterns in Data Using Machine Learning
• Generalized Linear Models
• How to Implement Gradient Descent Optimization from Scratch
• Interpreting Correlation Coefficients
• Image Captioning Using Machine Learning
• fit() vs predict() vs fit_predict() in Python scikit-learn
• CNN Filters
• Shannon Entropy
• Time Series -Exponential Smoothing
• AUC ROC Curve in Machine Learning
• Vector Norms in Machine Learning
• Swarm Intelligence
• L1 and L2 Regularization Methods in Machine Learning
• ML Approaches for Time Series
• MSE and Bias-Variance Decomposition
• Simple Exponential Smoothing
• How to Optimise Machine Learning Model
• Multiclass logistic regression from scratch
• Lightbm Multilabel Classification
• Monte Carlo Methods
• What is Inverse Reinforcement learning
• Content-Based Recommender System
• Context-Awareness Recommender System
• Predicting Flights Using Machine Learning
• NTLK Corpus
• Traditional Feature Engineering Models
• Concept Drift and Model Decay in Machine Learning
• Hierarchical Reinforcement Learning
• What is Feature Scaling and Why is it Important in Machine Learning
• Difference between Statistical Model and Machine Learning
• Introduction to Ranking Algorithms in Machine Learning
• Multicollinearity: Causes, Effects and Detection
• Bag of N-Grams Model
• TF-IDF Model

Related Tutorials

• Tensorflow Tutorial
• PyTorch Tutorial
• Data Science Tutorial
• AI Tutorial
• NLP Tutorial
• Reinforcement Learning

Interview Questions

• Machine learning Interview

The hypothesis is a common term in Machine Learning and data science projects. As we know, machine learning is one of the most powerful technologies across the world, which helps us to predict results based on past experiences. Moreover, data scientists and ML professionals conduct experiments that aim to solve a problem. These ML professionals and data scientists make an initial assumption for the solution of the problem. This assumption in Machine learning is known as Hypothesis. In Machine Learning, at various times, Hypothesis and Model are used interchangeably. However, a Hypothesis is an assumption made by scientists, whereas a model is a mathematical representation that is used to test the hypothesis. In this topic, "Hypothesis in Machine Learning," we will discuss a few important concepts related to a hypothesis in machine learning and their importance. So, let's start with a quick introduction to Hypothesis. It is just a guess based on some known facts but has not yet been proven. A good hypothesis is testable, which results in either true or false. : Let's understand the hypothesis with a common example. Some scientist claims that ultraviolet (UV) light can damage the eyes then it may also cause blindness. In this example, a scientist just claims that UV rays are harmful to the eyes, but we assume they may cause blindness. However, it may or may not be possible. Hence, these types of assumptions are called a hypothesis. The hypothesis is one of the commonly used concepts of statistics in Machine Learning. It is specifically used in Supervised Machine learning, where an ML model learns a function that best maps the input to corresponding outputs with the help of an available dataset. There are some common methods given to find out the possible hypothesis from the Hypothesis space, where hypothesis space is represented by and hypothesis by Th ese are defined as follows: It is used by supervised machine learning algorithms to determine the best possible hypothesis to describe the target function or best maps input to output. It is often constrained by choice of the framing of the problem, the choice of model, and the choice of model configuration. . It is primarily based on data as well as bias and restrictions applied to data. Hence hypothesis (h) can be concluded as a single hypothesis that maps input to proper output and can be evaluated as well as used to make predictions. The hypothesis (h) can be formulated in machine learning as follows: Where, Y: Range m: Slope of the line which divided test data or changes in y divided by change in x. x: domain c: intercept (constant) : Let's understand the hypothesis (h) and hypothesis space (H) with a two-dimensional coordinate plane showing the distribution of data as follows: Hypothesis space (H) is the composition of all legal best possible ways to divide the coordinate plane so that it best maps input to proper output. Further, each individual best possible way is called a hypothesis (h). Hence, the hypothesis and hypothesis space would be like this: Similar to the hypothesis in machine learning, it is also considered an assumption of the output. However, it is falsifiable, which means it can be failed in the presence of sufficient evidence. Unlike machine learning, we cannot accept any hypothesis in statistics because it is just an imaginary result and based on probability. Before start working on an experiment, we must be aware of two important types of hypotheses as follows: A null hypothesis is a type of statistical hypothesis which tells that there is no statistically significant effect exists in the given set of observations. It is also known as conjecture and is used in quantitative analysis to test theories about markets, investment, and finance to decide whether an idea is true or false. An alternative hypothesis is a direct contradiction of the null hypothesis, which means if one of the two hypotheses is true, then the other must be false. In other words, an alternative hypothesis is a type of statistical hypothesis which tells that there is some significant effect that exists in the given set of observations. The significance level is the primary thing that must be set before starting an experiment. It is useful to define the tolerance of error and the level at which effect can be considered significantly. During the testing process in an experiment, a 95% significance level is accepted, and the remaining 5% can be neglected. The significance level also tells the critical or threshold value. For e.g., in an experiment, if the significance level is set to 98%, then the critical value is 0.02%. The p-value in statistics is defined as the evidence against a null hypothesis. In other words, P-value is the probability that a random chance generated the data or something else that is equal or rarer under the null hypothesis condition. If the p-value is smaller, the evidence will be stronger, and vice-versa which means the null hypothesis can be rejected in testing. It is always represented in a decimal form, such as 0.035. Whenever a statistical test is carried out on the population and sample to find out P-value, then it always depends upon the critical value. If the p-value is less than the critical value, then it shows the effect is significant, and the null hypothesis can be rejected. Further, if it is higher than the critical value, it shows that there is no significant effect and hence fails to reject the Null Hypothesis. In the series of mapping instances of inputs to outputs in supervised machine learning, the hypothesis is a very useful concept that helps to approximate a target function in machine learning. It is available in all analytics domains and is also considered one of the important factors to check whether a change should be introduced or not. It covers the entire training data sets to efficiency as well as the performance of the models. Hence, in this topic, we have covered various important concepts related to the hypothesis in machine learning and statistics and some important parameters such as p-value, significance level, etc., to understand hypothesis concepts in a better way.

Latest Courses

We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks

Contact info

G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India

[email protected] .

Online Compiler

'Hypothesis space search' presentation slideshows

Hypothesis space search - powerpoint ppt presentation.

Classification with Decision Trees and Rules

Classification with Decision Trees and Rules. Evgueni Smirnov. Overview. Classification Problem Decision Trees for Classification Decision Rules for Classification. Classification Task. Given:

1.09k views • 80 slides

Decision-Tree Induction & Decision-Rule Induction

Decision-Tree Induction & Decision-Rule Induction. Evgueni Smirnov . Overview. Instances, Concepts, Languages Types of Tasks Decision Trees Decision Rules Evaluation Techniques Intro to Weka . Instances, Concepts, Languages.

1.19k views • 64 slides

Machine Learning Chapter 3. Decision Tree Learning

Machine Learning Chapter 3. Decision Tree Learning. Tom M. Mitchell. Abstract. Decision tree representation ID3 learning algorithm Entropy, Information gain Overfitting. Decision Tree for PlayTennis. A Tree to Predict C-Section Risk. Learned from medical records of 1000 women

905 views • 29 slides

Decision Tree Learning

Decision Tree Learning. Presented by Ping Zhang Nov. 26th, 2007. Introduction. Decision tree learning is one of the most widely used and practical method for inductive inference

612 views • 22 slides

Machine Learning: Lecture 3

Machine Learning: Lecture 3. Decision Tree Learning (Based on Chapter 3 of Mitchell T.., Machine Learning, 1997). Decision Tree Representation. Outlook. Sunny. Rain. Overcast. Humidity Wind. Yes. High. Weak. Normal. Strong. Yes. No. Yes. No.

156 views • 13 slides

View Hypothesis space search PowerPoint (PPT) presentations online in SlideServe. SlideServe has a very huge collection of Hypothesis space search PowerPoint presentations. You can view or download Hypothesis space search presentations for your school assignment or business presentation. Browse for the presentations on every topic that you want.