python Decision Tree, add need visualization the process

Author: xlisp

DecisionTreeClassifier.png

@@ -0,0 +1,67 @@
+# --- python Decision Tree, add need visualization the process
+
+# To visualize the decision-making process of the Decision Tree, we can use `plot_tree` from `scikit-learn`. This function generates a plot of the tree structure, showing the conditions at each node and the splits made during training.
+
+# Here’s an extended version of the previous example with a visualization of the decision tree:
+
+# ### Full Example with Visualization:
+
+# ```python
+# Import required libraries
+import matplotlib.pyplot as plt
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier, plot_tree
+from sklearn import metrics
+
+# Load the dataset (Iris dataset in this case)
+iris = load_iris()
+X = iris.data  # Features
+y = iris.target  # Labels
+
+# Split the dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+# Initialize the Decision Tree Classifier
+clf = DecisionTreeClassifier()
+
+# Train the model
+clf.fit(X_train, y_train)
+
+# Make predictions
+y_pred = clf.predict(X_test)
+
+# Evaluate the model
+accuracy = metrics.accuracy_score(y_test, y_pred)
+print(f"Accuracy: {accuracy * 100:.2f}%")
+
+# Visualize the decision tree
+plt.figure(figsize=(12,8))
+plot_tree(clf, feature_names=iris.feature_names, class_names=iris.target_names, filled=True)
+plt.show()
+# ```
+
+# ### Key Explanation:
+# 1. **`plot_tree` Function**:
+#    - `clf`: The trained decision tree classifier.
+#    - `feature_names`: Names of the features (columns) in the dataset.
+#    - `class_names`: Labels of the target variable (class names).
+#    - `filled=True`: Colors the nodes to indicate different classes.
+#
+# 2. **`plt.figure(figsize=(12,8))`**: Resizes the plot for better visualization.
+
+# ### Visualization Output:
+# The visualization will show a tree where each node contains:
+# - The feature used for splitting.
+# - The threshold for the split.
+# - The Gini impurity or entropy of the node.
+# - The number of samples at each node.
+# - The class distribution and the predicted class for leaf nodes.
+
+# This provides a clear visual of the decision process, showing how the tree splits data at each step.
+
+# Let me know if you'd like further customization for the tree's visualization!
+
+## python DecisionTreeClassifier.py
+# Matplotlib is building the font cache; this may take a moment.
+# Accuracy: 100.00%

DecisionTreeClassifier.py

@@ -11,6 +11,8 @@ torch = "^2.4.1"
 torchvision = "^0.19.1"
 tensorboard = "^2.17.1"
 numpy = "^2.1.1"
+scikit-learn = "^1.5.1"
+matplotlib = "^3.9.2"
 
 [build-system]
 requires = ["poetry-core"]