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Drawbacks Of Decision Tree.
Drawbacks Of Decision Tree Table Of Contents: Overfitting Instability Biased Towards Features with More Levels Difficulty with Continuous Variables Greedy Nature (Local Optima) Lack of Smooth Decision Boundaries Poor Performance on Unstructured Data Computational Complexity for Large Datasets Difficulty in Handling Correlated Features Interpretability Challenges for Large Trees (1) Overfitting (2) Instability (3) Biased Towards Features with More Levels (4) Difficulty with Continuous Variables (5) Greedy Nature (Local Optima) (6) Lack of Smooth Decision Boundaries (7) Poor Performance on Unstructured Data (8) Computational Complexity for Large Datasets (9) Difficulty in Handling Correlated Features (10) Interpretability Challenges for Large Trees
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Gini Index In Decision Tree.
What Is Gini Index? Table Of Contents: Introduction To Gini Index. Formula & Calculation. Gini Index In Decision Tree. Properties Of Gini Index. Weighted Gini Index. Gini Index Vs Entropy. Splitting Criteria and Gini Index. Advantages and Limitation Of Gini Index. Practical Implementation. Real-World Examples Advanced Topics (1) Introduction To Gini Index. Definition Of Gini Index In the context of decision trees, the Gini Index is a metric used to evaluate the purity of a split or node. It quantifies the probability of incorrectly classifying a randomly chosen element from the dataset if it were labeled randomly according to the
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All Machine Learning Algorithms To Study
All Machine Learning Algorithms To Study Table Of Contents: Regression Algorithms. Classification Algorithms. Clustering Algorithms. Ensemble Learning Algorithms. Dimensionality Reduction Algorithms Association Algorithms. Reinforcement Learning Algorithms. Deep Learning Algorithms. (1) Regression Algorithms Linear Regression. Regression Trees. Non-Linear Regression. Bayesian Linear Regression. Polynomial Regression. LASSO Regression. Ridge Regression. Weighted Least Squares Regression. (2) Classification Algorithms Logistic Regression Decision Trees Random Forest Support Vector Machines K – Nearest Neighbors Naive Bayes Algorithm (3) Clustering Algorithms K-Means Clustering K-Medoids (PAM) Hierarchical Clustering (Agglomerative and Divisive) DBSCAN (Density-Based Spatial Clustering of Applications with Noise) Mean Shift Clustering Gaussian Mixture Models (GMM) Spectral Clustering Affinity
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Gain Ratio In Decision Tree.
Gain Ratio In Decision Tree Table Of Contents: What Is Gain Ratio In Decision Tree? Example Of Gain Ratio Interpreting Split Information What Is The Range Of Gain Ratio? What We Want ? Balanced, Unbalanced & Moderate Split Which Split Information Is Better: Balanced, Unbalanced & Moderate Split How Gain Ratio Penalized Lower Split Information? Advantages Of Gain Ratio Disadvantages Of Gain Ratio (1) What Is Gain Ratio In Decision Tree? In decision tree learning, the Gain Ratio is an improvement over Information Gain to evaluate splits. While Information Gain measures the effectiveness of a feature in classifying the data,
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Information Gain In Decision Tree.
Information Gain Table Of Contents: What Is Information Gain? Advantages Of Information Gain. Limitation Of Information Gain. Information Gain: Advantages Of Information Gain: Limitation Of Information Gain:
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Attribute Selection Measures In Decision Tree.
What Are Attribute Selection Measures For Decision Tree? Table Of Contents: What Are Attribute Selection Measures For Decision Tree? What Are Attribute Selection Measures For Decision Tree? Attribute selection measures (ASMs) are criteria or metrics used in decision trees to determine the best attribute (or feature) to split the dataset at each node. The goal of these measures is to create pure child nodes by reducing uncertainty or impurity in the data, thereby improving the tree’s decision-making ability.
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How The Decision Tree Choses The Splitting Criteria If The Attribute Is Numeric?
How Decision Tree Choses The Splitting Criteria If The Attribute Is Numeric In Nature? Table Of Contents: How Decision Tree Choses The Splitting Criteria If The Attribute Is Numeric In Nature? How Decision Tree Choses The Splitting Criteria If The Attribute Is Numeric In Nature? When an attribute in a decision tree is numeric (e.g., age, salary, temperature), the splitting criteria involve finding an optimal threshold value to divide the data into two subsets. This is done to maximize the Information Gain (or other metrics like Gini Index). Below is the step-by-step explanation: Steps to Handle Numeric Attributes in Splitting
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How To Select Root Node For The Decision Tree?
How To Choose Root Node For Decision Tree? Table Of Contents: How To Choose Root Node For Decision Tree? How To Choose Root Node For Decision Tree? Let’s build a decision tree for a dataset to decide whether to Play Tennis based on conditions like weather, temperature, humidity, and wind. Step-1: Calculate The Overall Entropy Overall entropy tells us how much dataset is disordered initially. Step-2: Calculate Information Gain For Each Attribute. We now calculate the Information Gain for each attribute by splitting the dataset based on its values. Step-3: Choose the Attribute With The Highest Information Gain The attribute
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Topics To Learn In Decision Tree.
Topics To Learn In Decision Tree. Table Of Contents: Basic Understanding of Decision Trees. Splitting Criteria. Building a Decision Tree. Overfitting and Underfitting. Hyperparameters of Decision Trees. Handling Categorical Data. Evaluation Metrics for Decision Trees. Practical Implementation. Real-world Use Cases. Advancement In Decision Tree. (1) Basic Understanding of Decision Trees. (2) Splitting Criteria (3) Building a Decision Tree (4) Overfitting and Underfitting (5) Hyperparameters of Decision Trees. (6) Handling Categorical Data. (7) Evaluation Metrics for Decision Trees (8) Practical Implementation (9) Real-world Use Cases (10) Advanced Topics
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Interview Questions On Linear Regression.
Interview Questions On Linear Regression. Table Of Contents: What is linear regression? Explain the assumptions of a linear regression model. What is the difference between simple and multiple linear regression? What is multicollinearity, and how do you detect it? What are residuals in linear regression? What is the cost function used in linear regression? How do you find the optimal parameters in linear regression? Explain the formula for the regression line. What is R-squared? What is the adjusted R-squared, and why is it important? How do you handle categorical variables in linear regression? What would you do if your model
