Data Science - Part V - Decision Trees & Random Forests Derek Kane
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
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.
Data Science - Part V - Decision Trees & Random Forests Derek Kane
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
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 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.
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning.
Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
Using Classification and Clustering with Azure Machine Learning Models shows how to use classification and clustering algorithms with Azure Machine Learning.
Decision tree in artificial intelligenceMdAlAmin187
Decision tree.
Decision Tree that based on artificial intelligence. The main ideas behind Decision Trees were invented more than 70 years ago, and nowadays they are among the most powerful Machine Learning tools.
DBScan stands for Density-Based Spatial Clustering of Applications with Noise.
DBScan Concepts
DBScan Parameters
DBScan Connectivity and Reachability
DBScan Algorithm , Flowchart and Example
Advantages and Disadvantages of DBScan
DBScan Complexity
Outliers related question and its solution.
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
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.
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning.
Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
Using Classification and Clustering with Azure Machine Learning Models shows how to use classification and clustering algorithms with Azure Machine Learning.
Decision tree in artificial intelligenceMdAlAmin187
Decision tree.
Decision Tree that based on artificial intelligence. The main ideas behind Decision Trees were invented more than 70 years ago, and nowadays they are among the most powerful Machine Learning tools.
DBScan stands for Density-Based Spatial Clustering of Applications with Noise.
DBScan Concepts
DBScan Parameters
DBScan Connectivity and Reachability
DBScan Algorithm , Flowchart and Example
Advantages and Disadvantages of DBScan
DBScan Complexity
Outliers related question and its solution.
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
Machine Learning Unit-5 Decesion Trees & Random Forest.pdfAdityaSoraut
Its all about Machine learning .Machine learning is a field of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit programming instructions. Instead, these algorithms learn from data, identifying patterns, and making decisions or predictions based on that data.
There are several types of machine learning approaches, including:
Supervised Learning: In this approach, the algorithm learns from labeled data, where each example is paired with a label or outcome. The algorithm aims to learn a mapping from inputs to outputs, such as classifying emails as spam or not spam.
Unsupervised Learning: Here, the algorithm learns from unlabeled data, seeking to find hidden patterns or structures within the data. Clustering algorithms, for instance, group similar data points together without any predefined labels.
Semi-Supervised Learning: This approach combines elements of supervised and unsupervised learning, typically by using a small amount of labeled data along with a large amount of unlabeled data to improve learning accuracy.
Reinforcement Learning: This paradigm involves an agent learning to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties, enabling it to learn the optimal behavior to maximize cumulative rewards over time.Machine learning algorithms can be applied to a wide range of tasks, including:
Classification: Assigning inputs to one of several categories. For example, classifying whether an email is spam or not.
Regression: Predicting a continuous value based on input features. For instance, predicting house prices based on features like square footage and location.
Clustering: Grouping similar data points together based on their characteristics.
Dimensionality Reduction: Reducing the number of input variables to simplify analysis and improve computational efficiency.
Recommendation Systems: Predicting user preferences and suggesting items or actions accordingly.
Natural Language Processing (NLP): Analyzing and generating human language text, enabling tasks like sentiment analysis, machine translation, and text summarization.
Machine learning has numerous applications across various domains, including healthcare, finance, marketing, cybersecurity, and more. It continues to be an area of active research and
Lecture 9 - Decision Trees and Ensemble Methods, a lecture in subject module ...Maninda Edirisooriya
Decision Trees and Ensemble Methods is a different form of Machine Learning algorithm classes. This was one of the lectures of a full course I taught in University of Moratuwa, Sri Lanka on 2023 second half of the year.
Machine learning session6(decision trees random forrest)Abhimanyu Dwivedi
Concepts include decision tree with its examples. Measures used for splitting in decision tree like gini index, entropy, information gain, pros and cons, validation. Basics of random forests with its example and uses.
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.
Decision Trees for Classification: A Machine Learning AlgorithmPalin analytics
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.
Decision tree knowledge discovery through neural Networks
structure of decision tree and neural networks.
how they work?
Models
working
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ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
An ensemble is itself a supervised learning algorithm, because it can be trained and then used to make predictions. The trained ensemble, therefore, represents a single hypothesis. This hypothesis, however, is not necessarily contained within the hypothesis space of the models from which it is built.
DCOM (Distributed Component Object Model) and CORBA (Common Object Request Broker Architecture) are two popular distributed object models. In this paper, we make architectural comparison of DCOM and CORBA at three different layers: basic programming architecture, remoting architecture, and the wire protocol architecture.
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2. Decision Tree Algorithm
A decision tree is a flowchart-like tree structure where an internal node represents feature(or attribute), the branch
represents a decision rule, and each leaf node represents the outcome.
The topmost node in a decision tree is known as the root node. It learns to partition on the basis of the attribute
value. It partitions the tree in recursively manner call recursive partitioning. This flowchart-like structure helps you in
decision making.
3. Decision Tree Algorithm
Decision Tree is a white box type of ML algorithm. It shares internal decision-making logic, which makes them one of
the most interpretable algorithm in machine learning.
Its training time is faster compared to the neural network algorithm. The decision tree is a distribution-free or non-
parametric method, which does not depend upon probability distribution assumptions. Decision trees can handle
high dimensional data with good accuracy.
Tree based learning algorithms are considered to be one of the best and mostly used supervised learning methods.
Tree based methods empower predictive models with high accuracy, stability and ease of interpretation. Unlike
linear models, they map non-linear relationships quite well.
4. Common Terms
• Root Node: It represents entire population or sample and this further gets divided into two or more
homogeneous sets.
• Splitting: It is a process of dividing a node into two or more sub-nodes.
• Decision Node: When a sub-node splits into further sub-nodes, then it is called decision node.
• Leaf/ Terminal Node: Nodes that do not split are called Leaf or Terminal node.
• Pruning: When we remove sub-nodes of a decision node, this process is called pruning. You can say opposite
process of splitting.
• Branch / Sub-Tree: A sub section of entire tree is called branch or sub-tree.
• Parent and Child Node: A node, which is divided into sub-nodes is called parent node of sub-nodes whereas sub-
nodes are the child of parent node.
5. Working of the Algo
1. Select the best attribute using Attribute Selection Measures(ASM) to split the records.
2. Make that attribute a decision node and breaks the dataset into smaller subsets.
3. Starts tree building by repeating this process recursively for each child until one of the condition will match:
1. All the tuples belong to the same attribute value.
2. There are no more remaining attributes
3. There are no more instances.
6. Attribute Selection Measures
Attribute selection measure is a heuristic for selecting the splitting criterion that partition data into the best possible
manner.
It is also known as splitting rules because it helps us to determine breakpoints for tuples on a given node.
ASM provides a rank to each feature(or attribute) by explaining the given dataset. Best score attribute will be
selected as a splitting attribute.
Most popular selection measures are Information Gain, Gini Index.
7. Information Gain
• Shannon invented the concept of entropy, which measures the impurity of the input set. In physics and
mathematics, entropy referred as the randomness or the impurity in the system. In information theory, it refers to
the impurity in a group of examples.
• Information gain is the decrease in entropy.
• Information gain computes the difference between entropy before split and average entropy after split of the
dataset based on given attribute values.
• ID3 (Iterative Dichotomiser) decision tree algorithm uses information gain.
• Less impure node requires less information to describe it. And, more impure node requires more information.
Information theory is a measure to define this degree of disorganization in a system known as Entropy.
• If the sample is completely homogeneous, then the entropy is zero and if the sample is an equally divided (50% —
50%), it has entropy of one.
• Entropy can be calculated using formula:- Entropy = -p log2 p — q log2q,
Where p and q is probability of success and failure respectively in that node.
• Entropy is also used with categorical target variable. It chooses the split which has lowest entropy compared to
parent node and other splits. The lesser the entropy, the better it is.
8. Information Gain
To build a decision tree, we need to calculate two types of entropy using frequency tables as follows:
1) Entropy using the frequency table of one attribute
2) Entropy using the frequency table of two attributes
11. Information Gain
The information gain is based on the decrease in entropy after a data-set is split on an attribute. Constructing a
decision tree is all about finding attribute that returns the highest information gain (i.e., the most homogeneous
branches).
Step 1: Calculate entropy of the target.
Step 2: The dataset is then split on the different attributes. The entropy for each branch is calculated. Then it is
added proportionally, to get total entropy for the split. The resulting entropy is subtracted from the entropy before
the split. The result is the Information Gain, or decrease in entropy.
13. Information Gain
Step 3: Choose attribute with the largest information gain as the decision node, divide the dataset by its branches
and repeat the same process on every branch.
16. Gini Index
Steps to Calculate Gini for a split:
1. Calculate Gini for sub-nodes, using formula sum of square of probability for success and failure (p²+q²).
2. Calculate Gini for split using weighted Gini score of each node of that split.
Referring to example where we want to segregate the students based on target variable ( playing cricket or not ). In
the snapshot below, we split the population using two input variables Gender and Class. Now, I want to identify
which split is producing more homogeneous sub-nodes using Gini index.
17. Gini Index
Split on Gender:
• Gini for sub-node Female = (0.2)*(0.2)+(0.8)*(0.8)=0.68
• Gini for sub-node Male = (0.65)*(0.65)+(0.35)*(0.35)=0.55
• Weighted Gini for Split Gender = (10/30)*0.68+(20/30)*0.55 = 0.59
18. Gini Index
Similar for Split on Class:
• Gini for sub-node Class IX = (0.43)*(0.43)+(0.57)*(0.57)=0.51
• Gini for sub-node Class X = (0.56)*(0.56)+(0.44)*(0.44)=0.51
• Weighted Gini for Split Class = (14/30)*0.51+(16/30)*0.51 = 0.51
19. Gini Index
Gini score for split on Gender: 0.59
Gini score on the split on Class: 0.51
Above, you can see that Gini score for Split on Gender is higher than Split on Class, hence, the node split will take
place on Gender.
20. Constraints on Tree Size
1. Minimum samples for a node split
• Defines the minimum number of samples (or observations) which are required in a node to be considered for
splitting.
• Used to control over-fitting. Higher values prevent a model from learning relations which might be highly specific
to the particular sample selected for a tree.
• Too high values can lead to under-fitting.
2. Minimum samples for a terminal node
• Defines the minimum samples (or observations) required in a terminal node or leaf.
• Used to control over-fitting similar to min_samples_split.
• Generally lower values should be chosen for imbalanced class problems because the regions in which the
minority class will be in majority will be very small.
21. Constraints on Tree Size
3. Maximum depth of tree
• The maximum depth of a tree.
• Used to control over-fitting as higher depth will allow model to learn relations very specific to a particular sample.
4. Maximum number of terminal nodes
• The maximum number of terminal nodes or leaves in a tree.
• Can be defined in place of max_depth. Since binary trees are created, a depth of ’n’ would produce a maximum of
2^n leaves.
5. Maximum features to consider for split
• The number of features to consider while searching for a best split. These will be randomly selected.
• As a thumb-rule, square root of the total number of features works great but we should check upto 30–40% of
the total number of features.
• Higher values can lead to over-fitting but depends on case to case.