Decision tree
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Decision tree
Decision trees are a type of supervised machine learning that use a tree-like model to predict target variables. They work by splitting data into smaller and smaller groups (branches) based on attribute values, continuing until the groups only contain similar target variable values or cannot be split further. The tree consists of decision nodes that test attributes, branches representing the outcome of the tests, and leaf nodes that represent classifications or predicted target values. The ID3 algorithm builds decision trees by selecting the attribute that creates the most information gain at each split in a greedy, top-down manner.
Decision tree
The document discusses decision tree algorithms. It begins with an introduction and example, then covers the principles of entropy and information gain used to build decision trees. It provides explanations of key concepts like entropy, information gain, and how decision trees are constructed and evaluated. Examples are given to illustrate these concepts. The document concludes with strengths and weaknesses of decision tree algorithms.
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2. Applications of Random Forest
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About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
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Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
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Decision tree
The document discusses decision tree algorithms. It begins with an introduction and example, then covers the principles of entropy and information gain used to build decision trees. It provides explanations of key concepts like evaluating decision trees using training and testing accuracy. The document concludes with strengths and weaknesses of decision tree algorithms.
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Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
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Decision tree
Decision trees are a type of supervised machine learning that use a tree-like model to predict target variables. They work by splitting data into smaller and smaller groups (branches) based on attribute values, continuing until the groups only contain similar target variable values or cannot be split further. The tree consists of decision nodes that test attributes, branches representing the outcome of the tests, and leaf nodes that represent classifications or predicted target values. The ID3 algorithm builds decision trees by selecting the attribute that creates the most information gain at each split in a greedy, top-down manner.
Decision tree
The document discusses decision tree algorithms. It begins with an introduction and example, then covers the principles of entropy and information gain used to build decision trees. It provides explanations of key concepts like entropy, information gain, and how decision trees are constructed and evaluated. Examples are given to illustrate these concepts. The document concludes with strengths and weaknesses of decision tree algorithms.
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A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
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Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
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Decision tree
The document discusses decision tree algorithms. It begins with an introduction and example, then covers the principles of entropy and information gain used to build decision trees. It provides explanations of key concepts like evaluating decision trees using training and testing accuracy. The document concludes with strengths and weaknesses of decision tree algorithms.
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Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
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No machine learning algorithm dominates in every domain, but random forests are usually tough to beat by much. And they have some advantages compared to other models. No much input preparation needed, implicit feature selection, fast to train, and ability to visualize the model. While it is easy to get started with random forests, a good understanding of the model is key to get the most of them.
This talk will cover decision trees from theory, to their implementation in scikit-learn. An overview of ensemble methods and bagging will follow, to end up explaining and implementing random forests and see how they compare to other state-of-the-art models.
The talk will have a very practical approach, using examples and real cases to illustrate how to use both decision trees and random forests.
We will see how the simplicity of decision trees, is a key advantage compared to other methods. Unlike black-box methods, or methods tough to represent in multivariate cases, decision trees can easily be visualized, analyzed, and debugged, until we see that our model is behaving as expected. This exercise can increase our understanding of the data and the problem, while making our model perform in the best possible way.
Random Forests can randomize and ensemble decision trees to increase its predictive power, while keeping most of their properties.
The main topics covered will include:
* What are decision trees?
* How decision trees are trained?
* Understanding and debugging decision trees
* Ensemble methods
* Bagging
* Random Forests
* When decision trees and random forests should be used?
* Python implementation with scikit-learn
* Analysis of performance
2.1 Data Mining-classification Basic concepts
This document discusses classification and decision trees. It defines classification as predicting categorical class labels using a model constructed from a training set. Decision trees are a popular classification method that operate in a top-down recursive manner, splitting the data into purer subsets based on attribute values. The algorithm selects the optimal splitting attribute using an evaluation metric like information gain at each step until it reaches a leaf node containing only one class.
Decision Tree Learning
This presentation was prepared as part of the curriculum studies for CSCI-659 Topics in Artificial Intelligence Course - Machine Learning in Computational Linguistics.
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This Edureka Decision Tree tutorial will help you understand all the basics of Decision tree. This decision tree tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn decision tree analysis along with examples.
Below are the topics covered in this tutorial:
1) Machine Learning Introduction
2) Classification
3) Types of classifiers
4) Decision tree
5) How does Decision tree work?
6) Demo in R
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
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This document discusses decision tree algorithms C4.5 and CART. It explains that ID3 has limitations in dealing with continuous data and noisy data, which C4.5 aims to address through techniques like post-pruning trees to avoid overfitting. CART uses binary splits and measures like Gini index or entropy to produce classification trees, and sum of squared errors to produce regression trees. It also performs cost-complexity pruning to find an optimal trade-off between accuracy and model complexity.
Decision Trees
Decision trees are a type of supervised learning algorithm used for classification and regression. ID3 and C4.5 are algorithms that generate decision trees by choosing the attribute with the highest information gain at each step. Random forest is an ensemble method that creates multiple decision trees and aggregates their results, improving accuracy. It introduces randomness when building trees to decrease variance.
Decision tree
The document discusses various decision tree learning methods. It begins by defining decision trees and issues in decision tree learning, such as how to split training records and when to stop splitting. It then covers impurity measures like misclassification error, Gini impurity, information gain, and variance reduction. The document outlines algorithms like ID3, C4.5, C5.0, and CART. It also discusses ensemble methods like bagging, random forests, boosting, AdaBoost, and gradient boosting.
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Decision tree
This document provides an overview of decision trees, including:
- Decision trees classify records by sorting them down the tree from root to leaf node, where each leaf represents a classification outcome.
- Trees are constructed top-down by selecting the most informative attribute to split on at each node, usually based on information gain.
- Trees can handle both numerical and categorical data and produce classification rules from paths in the tree.
- Examples of decision tree algorithms like ID3 that use information gain to select the best splitting attribute are described. The concepts of entropy and information gain are defined for selecting splits.
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The document discusses decision trees and how they work. It begins with explaining what a decision tree is - a tree-shaped diagram used to determine a course of action, with each branch representing a possible decision. It then provides examples of using a decision tree to classify vegetables and animals based on their features. The document also covers key decision tree concepts like entropy, information gain, leaf nodes, decision nodes, and the root node. It demonstrates how a decision tree is built by choosing splits that maximize information gain. Finally, it presents a use case of using a decision tree to predict loan repayment.
Random forest algorithm
The document discusses the random forest algorithm. It introduces random forest as a supervised classification algorithm that builds multiple decision trees and merges them to provide a more accurate and stable prediction. It then provides an example pseudocode that randomly selects features to calculate the best split points to build decision trees, repeating the process to create a forest of trees. The document notes key advantages of random forest are that it avoids overfitting and can be used for both classification and regression tasks.
From decision trees to random forests
This document discusses decision trees and random forests for classification problems. It explains that decision trees use a top-down approach to split a training dataset based on attribute values to build a model for classification. Random forests improve upon decision trees by growing many de-correlated trees on randomly sampled subsets of data and features, then aggregating their predictions, which helps avoid overfitting. The document provides examples of using decision trees to classify wine preferences, sports preferences, and weather conditions for sport activities based on attribute values.
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Below topics are explained in this Decision tree in R presentation :
1. What is Decision tree?
2. What problems can be solved using Decision Trees?
3. How does a Decision Tree work?
4. Use case: Survival prediction in R
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modelling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbours, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course
Classification techniques in data mining
The document discusses classification algorithms in machine learning. It provides an overview of various classification algorithms including decision tree classifiers, rule-based classifiers, nearest neighbor classifiers, Bayesian classifiers, and artificial neural network classifiers. It then describes the supervised learning process for classification, which involves using a training set to construct a classification model and then applying the model to a test set to classify new data. Finally, it provides a detailed example of how a decision tree classifier is constructed from a training dataset and how it can be used to classify data in the test set.
Random forest
The document discusses random forest, an ensemble classifier that uses multiple decision tree models. It describes how random forest works by growing trees using randomly selected subsets of features and samples, then combining the results. The key advantages are better accuracy compared to a single decision tree, and no need for parameter tuning. Random forest can be used for classification and regression tasks.
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No machine learning algorithm dominates in every domain, but random forests are usually tough to beat by much. And they have some advantages compared to other models. No much input preparation needed, implicit feature selection, fast to train, and ability to visualize the model. While it is easy to get started with random forests, a good understanding of the model is key to get the most of them.
This talk will cover decision trees from theory, to their implementation in scikit-learn. An overview of ensemble methods and bagging will follow, to end up explaining and implementing random forests and see how they compare to other state-of-the-art models.
The talk will have a very practical approach, using examples and real cases to illustrate how to use both decision trees and random forests.
We will see how the simplicity of decision trees, is a key advantage compared to other methods. Unlike black-box methods, or methods tough to represent in multivariate cases, decision trees can easily be visualized, analyzed, and debugged, until we see that our model is behaving as expected. This exercise can increase our understanding of the data and the problem, while making our model perform in the best possible way.
Random Forests can randomize and ensemble decision trees to increase its predictive power, while keeping most of their properties.
The main topics covered will include:
* What are decision trees?
* How decision trees are trained?
* Understanding and debugging decision trees
* Ensemble methods
* Bagging
* Random Forests
* When decision trees and random forests should be used?
* Python implementation with scikit-learn
* Analysis of performance
2.1 Data Mining-classification Basic concepts
This document discusses classification and decision trees. It defines classification as predicting categorical class labels using a model constructed from a training set. Decision trees are a popular classification method that operate in a top-down recursive manner, splitting the data into purer subsets based on attribute values. The algorithm selects the optimal splitting attribute using an evaluation metric like information gain at each step until it reaches a leaf node containing only one class.
Decision Tree Learning
This presentation was prepared as part of the curriculum studies for CSCI-659 Topics in Artificial Intelligence Course - Machine Learning in Computational Linguistics.
It was prepared under guidance of Prof. Sandra Kubler.
Decision Tree Algorithm & Analysis | Machine Learning Algorithm | Data Scienc...
This Edureka Decision Tree tutorial will help you understand all the basics of Decision tree. This decision tree tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn decision tree analysis along with examples.
Below are the topics covered in this tutorial:
1) Machine Learning Introduction
2) Classification
3) Types of classifiers
4) Decision tree
5) How does Decision tree work?
6) Demo in R
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
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Random Forest is a supervised learning ensemble algorithm. Ensemble algorithms are those which combine more than one algorithms of same or different kind for classifying objects....
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This document discusses decision tree induction and attribute selection measures. It describes common measures like information gain, gain ratio, and Gini index that are used to select the best splitting attribute at each node in decision tree construction. It provides examples to illustrate information gain calculation for both discrete and continuous attributes. The document also discusses techniques for handling large datasets like SLIQ and SPRINT that build decision trees in a scalable manner by maintaining attribute value lists.
Decision Tree - C4.5&CART
This document discusses decision tree algorithms C4.5 and CART. It explains that ID3 has limitations in dealing with continuous data and noisy data, which C4.5 aims to address through techniques like post-pruning trees to avoid overfitting. CART uses binary splits and measures like Gini index or entropy to produce classification trees, and sum of squared errors to produce regression trees. It also performs cost-complexity pruning to find an optimal trade-off between accuracy and model complexity.
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Decision trees are a type of supervised learning algorithm used for classification and regression. ID3 and C4.5 are algorithms that generate decision trees by choosing the attribute with the highest information gain at each step. Random forest is an ensemble method that creates multiple decision trees and aggregates their results, improving accuracy. It introduces randomness when building trees to decrease variance.
Decision tree
The document discusses various decision tree learning methods. It begins by defining decision trees and issues in decision tree learning, such as how to split training records and when to stop splitting. It then covers impurity measures like misclassification error, Gini impurity, information gain, and variance reduction. The document outlines algorithms like ID3, C4.5, C5.0, and CART. It also discusses ensemble methods like bagging, random forests, boosting, AdaBoost, and gradient boosting.
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Decision Trees in Machine Learning - Decision tree method is a commonly used data mining method for establishing classification systems based on several covariates or for developing prediction algorithms for a target variable.
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This slide focuses on working procedure of some famous classification based machine learning algorithms
Decision tree
This document provides an overview of decision trees, including:
- Decision trees classify records by sorting them down the tree from root to leaf node, where each leaf represents a classification outcome.
- Trees are constructed top-down by selecting the most informative attribute to split on at each node, usually based on information gain.
- Trees can handle both numerical and categorical data and produce classification rules from paths in the tree.
- Examples of decision tree algorithms like ID3 that use information gain to select the best splitting attribute are described. The concepts of entropy and information gain are defined for selecting splits.
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The document discusses decision trees and how they work. It begins with explaining what a decision tree is - a tree-shaped diagram used to determine a course of action, with each branch representing a possible decision. It then provides examples of using a decision tree to classify vegetables and animals based on their features. The document also covers key decision tree concepts like entropy, information gain, leaf nodes, decision nodes, and the root node. It demonstrates how a decision tree is built by choosing splits that maximize information gain. Finally, it presents a use case of using a decision tree to predict loan repayment.
Random forest algorithm
The document discusses the random forest algorithm. It introduces random forest as a supervised classification algorithm that builds multiple decision trees and merges them to provide a more accurate and stable prediction. It then provides an example pseudocode that randomly selects features to calculate the best split points to build decision trees, repeating the process to create a forest of trees. The document notes key advantages of random forest are that it avoids overfitting and can be used for both classification and regression tasks.
From decision trees to random forests
This document discusses decision trees and random forests for classification problems. It explains that decision trees use a top-down approach to split a training dataset based on attribute values to build a model for classification. Random forests improve upon decision trees by growing many de-correlated trees on randomly sampled subsets of data and features, then aggregating their predictions, which helps avoid overfitting. The document provides examples of using decision trees to classify wine preferences, sports preferences, and weather conditions for sport activities based on attribute values.
Decision Tree In R | Decision Tree Algorithm | Data Science Tutorial | Machin...
This presentation about Decision Tree Tutorial will help you understand what is decision tree, what problems can be solved using decision trees, how does a decision tree work and you will also see a use case implementation in which we do survival prediction using R. Decision tree is one of the most popular Machine Learning algorithms in use today, this is a supervised learning algorithm that is used for classifying problems. It works well classifying for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets based on the most significant attributes/ independent variables. In simple words, a decision tree is a tree shaped algorithm used to determine a course of action. Each branch of the tree represents a possible decision, occurrence or reaction. Now let us get started and understand how does Decision tree work.
Below topics are explained in this Decision tree in R presentation :
1. What is Decision tree?
2. What problems can be solved using Decision Trees?
3. How does a Decision Tree work?
4. Use case: Survival prediction in R
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modelling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbours, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course
Classification techniques in data mining
The document discusses classification algorithms in machine learning. It provides an overview of various classification algorithms including decision tree classifiers, rule-based classifiers, nearest neighbor classifiers, Bayesian classifiers, and artificial neural network classifiers. It then describes the supervised learning process for classification, which involves using a training set to construct a classification model and then applying the model to a test set to classify new data. Finally, it provides a detailed example of how a decision tree classifier is constructed from a training dataset and how it can be used to classify data in the test set.
Random forest
The document discusses random forest, an ensemble classifier that uses multiple decision tree models. It describes how random forest works by growing trees using randomly selected subsets of features and samples, then combining the results. The key advantages are better accuracy compared to a single decision tree, and no need for parameter tuning. Random forest can be used for classification and regression tasks.
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This document discusses decision trees and the ID3 algorithm for generating decision trees. It explains that a decision tree classifies examples based on their attributes through a series of questions or rules. The ID3 algorithm uses information gain to choose the most informative attributes to split on at each node, resulting in a tree that maximizes classification accuracy. Some drawbacks of decision trees are that they can only handle nominal attributes and may not be robust to noisy data.
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A decision tree is a guide to the potential results of a progression of related choices. It permits an individual or association to gauge potential activities against each other dependent on their costs, probabilities, and advantages. They can be utilized either to drive casual conversation or to outline a calculation that predicts the most ideal decision scientifically.
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Description and a basic understanding of Decision Trees algorithm used for classification and regression.
Decision Tree.pptx
This document provides an overview of decision tree classification algorithms. It defines key concepts like decision nodes, leaf nodes, splitting, pruning, and explains how a decision tree is constructed using attributes to recursively split the dataset into purer subsets. It also describes techniques like information gain and Gini index that help select the best attributes to split on, and discusses advantages like interpretability and disadvantages like potential overfitting.
10 decision tree
This document discusses algorithms for decision tree induction and building decision trees. It introduces the basic algorithm for constructing a decision tree in a top-down recursive manner by recursively partitioning training examples based on selected attributes. Attributes are selected using a heuristic like information gain that measures how well each attribute splits the data. The tree stops growing when examples at a node are all of one class or there are no remaining attributes. It also discusses using information theory and entropy to select the attribute that reduces uncertainty the most at each node by measuring the information content of answers.
Desicion tree and neural networks
Decision tree knowledge discovery through neural Networks
structure of decision tree and neural networks.
how they work?
Models
working
knowledge discovery
clustering
BUS308 – Week 1 Lecture 2 Describing Data Expected Out.docx
BUS308 – Week 1 Lecture 2
Describing Data
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Basic descriptive statistics for data location
2. Basic descriptive statistics for data consistency
3. Basic descriptive statistics for data position
4. Basic approaches for describing likelihood
5. Difference between descriptive and inferential statistics
What this lecture covers
This lecture focuses on describing data and how these descriptions can be used in an
analysis. It also introduces and defines some specific descriptive statistical tools and results.
Even if we never become a data detective or do statistical tests, we will be exposed and
bombarded with statistics and statistical outcomes. We need to understand what they are telling
us and how they help uncover what the data means on the “crime,” AKA research question/issue.
How we obtain these results will be covered in lecture 1-3.
Detecting
In our favorite detective shows, starting out always seems difficult. They have a crime,
but no real clues or suspects, no idea of what happened, no “theory of the crime,” etc. Much as
we are at this point with our question on equal pay for equal work.
The process followed is remarkably similar across the different shows. First, a case or
situation presents itself. The heroes start by understanding the background of the situation and
those involved. They move on to collecting clues and following hints, some of which do not pan
out to be helpful. They then start to build relationships between and among clues and facts,
tossing out ideas that seemed good but lead to dead-ends or non-helpful insights (false leads,
etc.). Finally, a conclusion is reached and the initial question of “who done it” is solved.
Data analysis, and specifically statistical analysis, is done quite the same way as we will
see.
Descriptive Statistics
Week 1 Clues
We are interested in whether or not males and females are paid the same for doing equal
work. So, how do we go about answering this question? The “victim” in this question could be
considered the difference in pay between males and females, specifically when they are doing
equal work. An initial examination (Doc, was it murder or an accident?) involves obtaining
basic information to see if we even have cause to worry.
The first action in any analysis involves collecting the data. This generally involves
conducting a random sample from the population of employees so that we have a manageable
data set to operate from. In this case, our sample, presented in Lecture 1, gave us 25 males and
25 females spread throughout the company. A quick look at the sample by HR provided us with
assurance that the group looked representative of the company workforce we are concerned with
as a whole. Now we can confidently collect clues to see if we should be concerned or not.
As with any detective, the first issue is to understand the.
Forms of learning in ai
Three main types of machine learning are supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model using labeled input/output data where the desired outputs are provided, allowing the model to map inputs to outputs. Unsupervised learning involves discovering hidden patterns in unlabeled data and grouping similar data points together. Reinforcement learning involves an agent learning through trial-and-error interactions with a dynamic environment by receiving rewards or punishments for actions.
Lt. 5 Pattern Reg.pptx
The document discusses decision trees and their use for classification. It begins by introducing decision trees and their structure, with inner nodes representing attributes and leaves representing classes. It then provides an example decision tree for classifying whether to play golf based on weather attributes. The document discusses how to classify new records using the tree by traversing it from the root node to a leaf node. It also discusses evaluating the accuracy of decision trees on test data and some advantages and shortcomings of decision tree classification.
Decision tree and random forest
The document discusses decision trees and random forest algorithms. It begins with an outline and defines the problem as determining target attribute values for new examples given a training data set. It then explains key requirements like discrete classes and sufficient data. The document goes on to describe the principles of decision trees, including entropy and information gain as criteria for splitting nodes. Random forests are introduced as consisting of multiple decision trees to help reduce variance. The summary concludes by noting out-of-bag error rate can estimate classification error as trees are added.
Handout11
Decision trees classify data using a series of binary tests on attributes. The CART framework is commonly used to design decision trees by greedily selecting tests that maximize decreases in impurity at each node. Trees are learned recursively by splitting nodes until reaching pure leaf nodes. Cross-validation is used to select an optimal stopping point to prevent overfitting and maximize generalization to new data.
Machine learning and decision trees
It gives a very detailed introduction about machine learning and decision tree construction.
Python code for decision tree classifier.
Statistics for management
This document provides an overview of key topics in statistics for management. It covers statistical surveys, classification and presentation of data, measures used to summarize data, probabilities, theoretical distributions, sampling and sampling distributions, estimation, hypothesis testing for large and small samples, and chi-square, F-distribution, analysis of variance, correlation, regression, business forecasting, and time series analysis. The document serves as an introduction to important statistical concepts and methods relevant for management.
Ch17 lab r_verdu103: Entry level statistics exercise (descriptives)
This document summarizes an R boot camp focusing on statistics. It includes an agenda that covers introducing the lab component, R basics, descriptive statistics in R, revisiting installation instructions, and measures of variability in R. Descriptive statistics are presented as ways to characterize data through measures of central tendency, shape, and variability. Examples are provided in R for calculating the mean, median, mode, range, percentiles, variance, standard deviation, and coefficient of variation. The central limit theorem and standardizing scores are also discussed. Real-world applications of R for clean and messy data are mentioned.
Data mining approaches and methods
This slide is about the data mining tasks and techniques.This slide consist of all the major mining task algorithms with their respective examples.
ML_Unit_1_Part_C
The document discusses decision tree learning and the ID3 algorithm. It begins by introducing decision trees and how they are used to classify instances by sorting them from the root node to a leaf node. It then discusses how ID3 builds decision trees in a top-down greedy manner by selecting the attribute that best splits the data at each node based on information gain. The document also covers issues like overfitting, handling continuous attributes, and pruning decision trees.
data
This document discusses analyzing and summarizing data. It defines key terms like data, variables, and different types of data including quantitative, qualitative, discrete, and continuous data. It also discusses different types of data analysis including descriptive, exploratory, inferential, predictive, causal, and mechanistic. Finally, it explains measures of central tendency including the mean, median, and mode. It provides examples and formulas for calculating each as well as their advantages and disadvantages.
Classifiers
Classifiers are algorithms that map input data to categories in order to build models for predicting unknown data. There are several types of classifiers that can be used including logistic regression, decision trees, random forests, support vector machines, Naive Bayes, and neural networks. Each uses different techniques such as splitting data, averaging predictions, or maximizing margins to classify data. The best classifier depends on the problem and achieving high accuracy, sensitivity, and specificity.
coppin chapter 10e.ppt
The document provides an overview of machine learning concepts including training, rote learning, concept learning, hypotheses, general to specific ordering, version spaces, candidate elimination, inductive bias, decision tree induction, overfitting, the nearest neighbor algorithm, neural networks, supervised learning, unsupervised learning, and reinforcement learning. It discusses how machine learning systems are trained using classified data and how different algorithms like concept learning, decision trees, nearest neighbors and neural networks can be used to classify new unlabeled data.
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Decision tree
- 2. Content What is decision tree? Example How to select the deciding node? Entropy Information gain Gini Impurity Steps for Making decision tree Pros. Cons.
- 3. What is decision tree? Decision tree is the most powerful and popular tool for classification and prediction. A Decision tree is a flowchart like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label. It is a type of superised learning algorithm. It is one of the most widely used and practical method for inductive inference
- 4. Example Let's assume we want to play badminton on a particular day — say Saturday — how will you decide whether to play or not. Let's say you go out and check if it's hot or cold, check the speed of the wind and humidity, how the weather is, i.e. is it sunny, cloudy, or rainy. You take all these factors into account to decide if you want to play or not.
- 5. So, you calculate all these factors for the last ten days and form a lookup table like the one below. Day Weather Temperature Humidity Wind 1 Sunny Hot High Weak 2 Cloudy Hot High Weak 3 Sunny Mild Normal Strong 4 Cloudy Mild High Strong 5 Rainy Mild High Strong 6 Rainy Cool Normal Strong 7 Rainy Mild High Weak 8 Sunny Hot High Strong 9 Cloudy Hot Normal Weak 10 Rainy Mild High Strong
- 6. A decision tree would be a great way to represent data like this because it takes into account all the possible paths that can lead to the final decision by following a tree-like structure.
- 7. How to select the deciding node? Which is the best Classifier?
- 8. So,we can conclude Less impure node requires less information to describe it. More impure node requires more information. Information theory is a measure to define this degree of disorganization in a system known as Entropy.
- 9. Entropy - measuring homogeneity of a learning set Entropy is a measure of the uncertainty about a source of messages. Given a collection S, containing positive and negative examples of some target concept, the entropy of S relative to this classification. where, pi is the proportion of S belonging to class i
- 10. Entropy is 0 if all the members of S belong to the same class. Entropy is 1 when the collection contains an equal no. of +ve and -ve examples. Entropy is between 0 and 1 if the collection contains unequal no. of +ve and -ve examples.
- 11. Information gain Decides which attribute goes into a decision node. To minimize the decision tree depth, the attribute with the most entropy reduction is the best choice! They are of 2 types- 1. High Information Gain 2. Low Information Gain
- 12. The information gain, Gain(S,A) of an attribute . Where: S is each value v of all possible values of attributeA Sv = subset of S for which attribute A has valuev |Sv| = number of elements in Sv |S| = number of elements in S
- 13. High Information Gain An attribute with high information gain splits the data into groups with an uneven number of positives and negatives and as a result helps in separating the two from each other.
- 14. Low Information Gain An attribute with low information gain splits the data relatively evenly and as a result doesn’t bring us any closer to a decision.
- 15. Gini Impurity Gini Impurity is a measurement of the likelihood of an incorrect classification of a new instance of a random variable, if that new instance were randomly classified according to the distribution of class labels from the data set. If our dataset is Pure then likelihood of incorrect classification is 0. If our sample is mixture of different classes then likelihood of incorrect classification will be high. They are of 2 types Pure means, in a selected sample of dataset all data belongs to same class. Impure means, data is mixture of different classes.
- 16. Steps for Making decision tree Get list of rows (dataset) which are taken into consideration for making decision tree (recursively at each nodes). Calculate uncertanity of our dataset or Gini impurity or how much our data is mixed up etc. Generate list of all question which needs to be asked at that node. Partition rows into True rows and False rows based on each question asked. Calculate information gain based on gini impurity and partition of data from previous step. Update highest information gain based on each question asked. Update best question based on information gain (higher information gain). Divide the node on best question. Repeat again from step 1 again until we get pure node (leaf
- 17. Pros. Easy to use and understand. Can handle both categorical and numerical data. Resistant to outliers, hence require little data preprocessing.
- 18. Cons. Prone to overfitting. Require some kind of measurement as to how well they are doing. Need to be careful with parameter tuning. Can create biased learned trees if some classes dominate.