feature: add a comparison between all algorithms for each dataset to see which performs best
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============================================================
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IRIS
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============================================================
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--- Decision Tree ---
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Accuracy: 1.000
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Adj. Rand: 1.000
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precision recall f1-score support
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setosa 1.00 1.00 1.00 19
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versicolor 1.00 1.00 1.00 13
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virginica 1.00 1.00 1.00 13
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accuracy 1.00 45
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macro avg 1.00 1.00 1.00 45
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weighted avg 1.00 1.00 1.00 45
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--- Naive Bayes ---
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Accuracy: 0.978
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Adj. Rand: 0.943
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precision recall f1-score support
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setosa 1.00 1.00 1.00 19
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versicolor 1.00 0.92 0.96 13
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virginica 0.93 1.00 0.96 13
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accuracy 0.98 45
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macro avg 0.98 0.97 0.97 45
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weighted avg 0.98 0.98 0.98 45
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--- K-Means (mapped) ---
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Accuracy: 0.893
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Adj. Rand: 0.730
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precision recall f1-score support
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setosa 1.00 1.00 1.00 50
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versicolor 0.77 0.96 0.86 50
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virginica 0.95 0.72 0.82 50
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accuracy 0.89 150
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macro avg 0.91 0.89 0.89 150
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weighted avg 0.91 0.89 0.89 150
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============================================================
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DIGITS
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============================================================
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--- Decision Tree ---
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Accuracy: 0.843
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Adj. Rand: 0.685
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precision recall f1-score support
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0 0.92 0.91 0.91 53
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1 0.74 0.78 0.76 50
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2 0.83 0.74 0.79 47
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3 0.78 0.85 0.81 54
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4 0.81 0.85 0.83 60
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5 0.92 0.86 0.89 66
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6 0.93 0.94 0.93 53
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7 0.85 0.84 0.84 55
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8 0.89 0.77 0.82 43
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9 0.78 0.85 0.81 59
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accuracy 0.84 540
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macro avg 0.85 0.84 0.84 540
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weighted avg 0.85 0.84 0.84 540
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--- Naive Bayes ---
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Accuracy: 0.852
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Adj. Rand: 0.710
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precision recall f1-score support
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0 1.00 0.98 0.99 53
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1 0.86 0.74 0.80 50
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2 0.86 0.66 0.75 47
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3 0.95 0.76 0.85 54
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4 0.98 0.85 0.91 60
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5 0.94 0.94 0.94 66
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6 0.89 0.96 0.93 53
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7 0.72 0.98 0.83 55
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8 0.57 0.91 0.70 43
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9 0.89 0.71 0.79 59
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accuracy 0.85 540
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macro avg 0.87 0.85 0.85 540
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weighted avg 0.88 0.85 0.85 540
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--- K-Means (mapped) ---
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Accuracy: 0.794
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Adj. Rand: 0.667
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precision recall f1-score support
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0 0.99 0.99 0.99 178
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1 0.62 0.30 0.41 182
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2 0.84 0.84 0.84 177
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3 0.86 0.85 0.85 183
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4 0.99 0.92 0.95 181
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5 0.87 0.75 0.81 182
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6 0.97 0.98 0.98 181
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7 0.86 0.95 0.90 179
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8 0.45 0.59 0.51 174
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9 0.58 0.77 0.66 180
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accuracy 0.79 1797
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macro avg 0.80 0.79 0.79 1797
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weighted avg 0.80 0.79 0.79 1797
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@@ -0,0 +1,59 @@
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"""
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Compare Decision Tree, Naive Bayes (supervised) and K-Means (unsupervised)
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on the Iris and Digits datasets using the same metrics.
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"""
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from sklearn import datasets
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from sklearn.model_selection import train_test_split
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.naive_bayes import GaussianNB
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from sklearn.cluster import KMeans
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from sklearn.metrics import accuracy_score, classification_report, adjusted_rand_score
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import numpy as np
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def kmeans_accuracy(X, y, n_classes):
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"""Map each cluster to its majority true label, then compute accuracy."""
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kmeans = KMeans(n_clusters=n_classes, init="k-means++", n_init=10, random_state=42)
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kmeans.fit(X)
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labels = np.zeros_like(kmeans.labels_)
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for i in range(n_classes):
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mask = kmeans.labels_ == i
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if mask.sum() > 0:
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labels[mask] = np.bincount(y[mask]).argmax()
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return labels, kmeans
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def evaluate(name, dataset, target_names):
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print(f"\n{'='*60}")
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print(f" {name}")
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print(f"{'='*60}")
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X_train, X_test, y_train, y_test = train_test_split(
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dataset.data, dataset.target, test_size=0.3, random_state=42
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)
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# supervised
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for clf_name, clf in [("Decision Tree", DecisionTreeClassifier(random_state=42)),
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("Naive Bayes", GaussianNB())]:
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clf.fit(X_train, y_train)
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y_pred = clf.predict(X_test)
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print(f"\n--- {clf_name} ---")
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print(f"Accuracy: {accuracy_score(y_test, y_pred):.3f}")
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print(f"Adj. Rand: {adjusted_rand_score(y_test, y_pred):.3f}")
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print(classification_report(y_test, y_pred, target_names=target_names))
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# unsupervised (evaluated on full dataset)
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n_classes = len(target_names)
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mapped_labels, kmeans = kmeans_accuracy(dataset.data, dataset.target, n_classes)
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print(f"\n--- K-Means (mapped) ---")
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print(f"Accuracy: {accuracy_score(dataset.target, mapped_labels):.3f}")
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print(f"Adj. Rand: {adjusted_rand_score(dataset.target, kmeans.labels_):.3f}")
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print(classification_report(dataset.target, mapped_labels, target_names=target_names))
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iris = datasets.load_iris()
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evaluate("IRIS", iris, list(iris.target_names))
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digits = datasets.load_digits()
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evaluate("DIGITS", digits, [str(n) for n in digits.target_names])
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