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Hyper Parameters In Decision Tree.
Hyper Parameters In Decision Tree Table Of Contents: Maximum Depth (max_depth) Minimum Samples Split (min_samples_split) Minimum Samples per Leaf (min_samples_leaf) Maximum Features (max_features) Maximum Leaf Nodes (max_leaf_nodes) Minimum Impurity Decrease (min_impurity_decrease) Split Criterion (criterion) Random State (random_state) Class Weight (class_weight) Presort (presort) Splitter (splitter) (1) Maximum Depth (max_depth) from sklearn.datasets import load_iris from sklearn.tree import DecisionTreeClassifier, export_text from sklearn.tree import plot_tree import matplotlib.pyplot as plt # Load the Iris dataset iris = load_iris() X, y = iris.data, iris.target # Build a decision tree with max_depth=3 clf = DecisionTreeClassifier(max_depth=3, random_state=42) clf.fit(X, y) # Plot the decision tree plt.figure(figsize=(12, 8)) plot_tree(clf, feature_names=iris.feature_names,
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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
