Machine Learning Model Training Code

Certainly! Below is a Python code snippet that demonstrates how to train a Random Forest model using the `scikit-learn` library. This example includes data preprocessing, training, and evaluation of the model. For simplicity, let's use the well-known Iris dataset. ```python # Import necessary libraries import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.preprocessing import StandardScaler from sklearn.metrics import classification_report, accuracy_score, confusion_matrix from sklearn.datasets import load_iris # Load dataset iris = load_iris() X = pd.DataFrame(iris.data, columns=iris.feature_names) y = pd.Series(iris.target) # Split dataset into training and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y) # Preprocess the data scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.transform(X_test) # Train the Random Forest model model = RandomForestClassifier(n_estimators=100, random_state=42) model.fit(X_train_scaled, y_train) # Predict on the test set y_pred = model.predict(X_test_scaled) # Evaluate the model accuracy = accuracy_score(y_test, y_pred) conf_matrix = confusion_matrix(y_test, y_pred) class_report = classification_report(y_test, y_pred) # Print evaluation metrics print(f'Accuracy: {accuracy:.4f}') print('Confusion Matrix:') print(conf_matrix) print('Classification Report:') print(class_report) # Feature importance feature_importances = pd.DataFrame(model.feature_importances_, index=iris.feature_names, columns=['Importance']).sort_values('Importance', ascending=False) print('Feature Importances:') print(feature_importances) ``` ### Explanation: 1. **Imports and Data Loading**: - Imports essential libraries such as `numpy`, `pandas`, and `scikit-learn` modules. - Loads the Iris dataset, a commonly used dataset for classification problems. 2. **Data Splitting**: - Splits the data into training and testing sets with a test size of 30%, ensuring the classes are evenly distributed using `stratify`. 3. **Data Preprocessing**: - Scales the features using `StandardScaler` to standardize the data. 4. **Model Training**: - Initializes a `RandomForestClassifier` with 100 trees and trains it on the scaled training data. 5. **Model Prediction and Evaluation**: - Predicts the labels for the test set. - Calculates and prints the accuracy, confusion matrix, and classification report. - Displays feature importances to understand which features contribute the most to the model. This code snippet provides a solid foundation for training and evaluating a Random Forest classifier, following best practices for machine learning.