How did you choose the model and hyperparameters, and how did you select the classification threshold?

Model and hyperparameter selection usually begins with analyzing the task and available data. Often, several basic models (e.g., logistic regression, random forest, gradient boosting) are tried first, and their quality is evaluated on a validation set.

Hyperparameters are tuned using search methods such as Grid Search or Random Search, sometimes with cross-validation for more reliable assessment. This helps find optimal settings that improve model quality.

The classification threshold is chosen based on metrics important for the task (e.g., F1-score, ROC-AUC, Precision-Recall). Typically, an ROC curve or Precision-Recall curve is plotted, and a threshold is selected that balances recall and precision according to business requirements.

Example in Python using sklearn:

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import precision_recall_curve

# Training the model with hyperparameter tuning
param_grid = {'n_estimators': [50, 100], 'max_depth': [5, 10]}
clf = GridSearchCV(RandomForestClassifier(), param_grid, cv=3)
clf.fit(X_train, y_train)

# Predicting probabilities
probs = clf.predict_proba(X_val)[:, 1]

# Selecting classification threshold
precision, recall, thresholds = precision_recall_curve(y_val, probs)
# Choose the threshold that maximizes F1
f1_scores = 2 * precision * recall / (precision + recall)
best_threshold = thresholds[f1_scores.argmax()]