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Function Signature

def evaluate_model(
    model: Any,
    X_train: Any,
    X_test: Any,
    y_train: Any,
    y_test: Any,
    model_name: str
) -> Tuple[Dict[str, Any], Any]

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Description

The evaluate_model() function evaluates a trained machine learning model by computing multiple performance metrics on both training and test datasets. It also performs 5-fold cross-validation to assess model generalization.

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Parameters

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model
Any
required
The trained machine learning model object. Must implement predict() method (e.g., scikit-learn models).
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X_train
Any
required
Training feature dataset. Typically a pandas DataFrame or numpy array containing the input features used to train the model.
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X_test
Any
required
Test feature dataset. Must have the same structure and feature columns as X_train.
​
y_train
Any
required
Training target values. The actual house prices (or other target variable) corresponding to X_train.
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y_test
Any
required
Test target values. The actual house prices corresponding to X_test, used to evaluate model performance.
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model_name
str
required
Human-readable name for the model (e.g., “Linear Regression”, “Decision Tree”). Used for identification in the metrics dictionary.

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Return Value

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return
Tuple[Dict[str, Any], Any]
Returns a tuple containing:
  1. Metrics Dictionary - Contains all computed performance metrics
  2. Test Predictions - The model’s predictions on the test set (y_test_pred)

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Metrics Dictionary Structure

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Cross-Validation Details

The function performs 5-fold cross-validation on the training data:
  • The training set is split into 5 equal parts (folds)
  • The model is trained on 4 folds and validated on the remaining fold
  • This process repeats 5 times, with each fold serving as the validation set once
  • The mean and standard deviation of R² scores across all folds are computed
  • This provides a robust estimate of model generalization capability

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Usage Example

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd

# Load and prepare data
df = pd.read_csv('house_prices.csv')
X = df[['area', 'bedrooms', 'bathrooms']]
y = df['price']

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Train model
model = LinearRegression()
model.fit(X_train, y_train)

# Evaluate model
metrics, predictions = evaluate_model(
    model=model,
    X_train=X_train,
    X_test=X_test,
    y_train=y_train,
    y_test=y_test,
    model_name="Linear Regression (Multivariate)"
)

# Access metrics
print(f"Test R²: {metrics['test_r2']:.4f}")
print(f"Test RMSE: {metrics['test_rmse']:.4f}")
print(f"CV R² Mean: {metrics['cv_r2_mean']:.4f}")

# Use predictions for further analysis
import matplotlib.pyplot as plt
plt.scatter(y_test, predictions)
plt.xlabel('Actual Prices')
plt.ylabel('Predicted Prices')
plt.show()

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