Overfitting occurs when a model fits noise or sample-specific variation instead of underlying patterns that generalize. In regression, too many parameters for the number of observations can create this failure, so adding variables does not automatically improve reliability. section 3.4.3
A review-score model
A model predicting customer review scores from delivery, product, and payment attributes should be treated cautiously when many predictors are included. A train/test workflow produced 0.43 accuracy and a confusion matrix with substantial misclassification, reinforcing the need to inspect results rather than assume the model is reliable. section 3.4.3
import pandas as pd
import mord
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, accuracy_score
# Prepare the data (assuming you have already merged necessary
DataFrames into analysis_df)
# Convert categorical variables to numerical format if required
# (e.g., using pd.get_dummies() for product categories)
# Split the data into training and testing sets
X = analysis_df.drop("review_score", axis=1)
y = analysis_df["review_score"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,
random_state=42)
# Fit the ordinal logistic regression model
model = mord.LogisticAT(alpha=0)
model.fit(X_train, y_train)
# Predict the review scores on the test data
y_pred = model.predict(X_test)
# Calculate the accuracy of the predictions
print("Accuracy:", accuracy_score(y_test, y_pred))
# Display the confusion matrix
print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
# Analyze the model's coefficients
print("Coefficients:\n", model.coef_)
Use the train/test prediction workflow to inspect accuracy and the confusion matrix before trusting a review-score model. section 3.4.3
