A logit is the natural logarithm of an event’s odds. In ordinal regression, coefficients describe how a predictor changes the log-odds of moving into a higher outcome category while other variables remain constant. section 3.4.3
Sleep and happiness
Suppose happiness is ordered as low, medium, and high. A positive sleep coefficient means that each additional hour of sleep increases the odds of moving to a higher happiness level; a negative coefficient means the reverse. Larger coefficients indicate stronger effects, assuming other conditions do not change. section 3.4.3
Read ordinal-regression logits as conditional associations:
- Hold other predictors constant when interpreting a coefficient. section 3.4.3
- Treat positive and negative signs as directional evidence, not automatic proof of causation. section 3.4.3
- Connect fitted coefficients to feature names before drawing business conclusions. 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_)
Inspect an ordinal logistic-regression workflow that fits the model, predicts ordered review scores, reports accuracy and a confusion matrix, and exposes coefficients. section 3.4.3
