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import numpy as np import pandas as pd from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, mean_squared_error from sklearn.preprocessing import StandardScaler iris = load_iris() X = iris.data[:, :1] # using only the first feature for simplicity y = (iris.target != 0) * 1 # converting to a binary classification problem (class 0 vs. classes 1 and 2) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) model = LogisticRegression() model.fit(X_train, y_train) y_pred = model.predict(X_test) cm = confusion_matrix(y_test, y_pred) print("Confusion Matrix:") print(cm) accuracy = accuracy_score(y_test, y_pred) print(f"Accuracy: {accuracy:.2f}") precision = precision_score(y_test, y_pred) print(f"Precision: {precision:.2f}") recall = recall_score(y_test, y_pred) print(f"Recall: {recall:.2f}") mse = mean_squared_error(y_test, y_pred) print(f"MSE: {mse:.2f}") rmse = np.sqrt(mse) print(f"RMSE: {rmse:.2f}")
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