Added mlp_activation_comparison.py

Added a new script (mlp_activation_comparison.py) that demonstrates the effect of different activation functions ('relu', 'tanh', 'logistic') on a simple dataset using scikit-learn's MLPClassifier. This helps visualize and understand how activation choices influence model performance.

Author: Karthikn-VR

machine_learning/mlp_activation_comparison.py

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+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.datasets import make_moons
+from sklearn.neural_network import MLPClassifier
+
+
+# Generate synthetic dataset
+X, y = make_moons(n_samples=200, noise=0.2, random_state=42)
+
+# Define classifiers with different activation functions
+classifiers = {
+    "relu": MLPClassifier(hidden_layer_sizes=(10, 10), activation="relu", max_iter=2000, random_state=42),
+    "tanh": MLPClassifier(hidden_layer_sizes=(10, 10), activation="tanh", max_iter=2000, random_state=42),
+    "logistic": MLPClassifier(hidden_layer_sizes=(10, 10), activation="logistic", max_iter=2000, random_state=42),
+}
+
+# Plot decision boundaries
+fig, axes = plt.subplots(1, 3, figsize=(15, 5))
+
+for ax, (name, clf) in zip(axes, classifiers.items()):
+    clf.fit(X, y)
+
+    # Create a mesh grid
+    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
+    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
+    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 200), np.linspace(y_min, y_max, 200))
+
+    # Predict on the mesh
+    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
+    Z = Z.reshape(xx.shape)
+
+    # Plot the decision boundary and training data
+    ax.contourf(xx, yy, Z, alpha=0.3)
+    ax.scatter(X[:, 0], X[:, 1], c=y, edgecolor="k", s=20)
+    ax.set_title(f"Activation: {name}")
+
+plt.tight_layout()
+plt.show()