Adjusting classification thresholds to hit specific metric targets.
Threshold hacking is a problematic practice in machine learning where practitioners manipulate classification thresholds solely to achieve specific performance metrics, rather than considering real-world impact.
This approach involves adjusting the probability cutoff point that determines when a model classifies something as positive or negative, without proper statistical or business justification. While threshold tuning itself is valid, threshold hacking aims only to hit arbitrary metric targets like accuracy or F1 score.
The danger lies in creating models that appear to perform well on paper but fail to generalize or provide meaningful business value. This often occurs when data scientists feel pressure to meet performance benchmarks without full consideration of the model's practical applications.
For new data scientists, this pattern can be particularly tempting when facing pressure to demonstrate model effectiveness. However, it typically leads to models that perform poorly in production, potentially damaging both business outcomes and professional credibility.
A better approach is to set thresholds based on careful analysis of business requirements, costs of different types of errors, and thorough validation across multiple metrics. This ensures models deliver real value rather than just impressive-looking numbers.
# Import necessary libraries
import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import precision_score
# Generate a synthetic classification dataset
X, y = make_classification(n_samples=1000, n_features=20, n_informative=15,
n_redundant=5, random_state=42)
# Split the dataset into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Initialize the logistic regression model
model = LogisticRegression(random_state=42, max_iter=1000)
# Train the model on the training set
model.fit(X_train, y_train)
# Get raw predicted probabilities for the positive class
y_proba = model.predict_proba(X_test)[:, 1]
# Define a range of thresholds to evaluate
thresholds = np.linspace(0.1, 0.9, 81)
# Track best precision score and corresponding threshold
best_precision = 0
best_threshold = 0
# Iterate over each threshold
print("Threshold Tuning Progress:")
print(f"{'Threshold':<10}{'Precision':<10}{'Best Precision':<15}{'Best Threshold':<15}")
for threshold in thresholds:
# Convert probabilities to binary predictions based on the current threshold
y_pred = (y_proba >= threshold).astype(int)
# Calculate precision score
precision = precision_score(y_test, y_pred)
# Check if this is the best precision score so far
if precision > best_precision:
best_precision = precision
best_threshold = threshold
# Report progress
print(f"{threshold:<10.2f}{precision:<10.2f}{best_precision:<15.2f}{best_threshold:<15.2f}")
# Final best score and threshold
print("\nFinal Results:")
print(f"Best Precision: {best_precision:.2f}")
print(f"Best Threshold: {best_threshold:.2f}")Example Output:
Threshold Tuning Progress:
Threshold Precision Best Precision Best Threshold
0.10 0.61 0.61 0.10
0.11 0.61 0.61 0.11
0.12 0.62 0.62 0.12
0.13 0.62 0.62 0.13
0.14 0.64 0.64 0.14
0.15 0.64 0.64 0.15
0.16 0.65 0.65 0.16
0.17 0.66 0.66 0.17
0.18 0.67 0.67 0.18
0.19 0.67 0.67 0.19
0.20 0.67 0.67 0.20
0.21 0.67 0.67 0.20
0.22 0.68 0.68 0.22
0.23 0.68 0.68 0.23
0.24 0.68 0.68 0.23
0.25 0.68 0.68 0.23
0.26 0.68 0.68 0.26
0.27 0.70 0.70 0.27
0.28 0.70 0.70 0.28
0.29 0.70 0.70 0.29
0.30 0.71 0.71 0.30
0.31 0.71 0.71 0.31
0.32 0.73 0.73 0.32
0.33 0.73 0.73 0.33
0.34 0.73 0.73 0.34
0.35 0.73 0.73 0.34
0.36 0.74 0.74 0.36
0.37 0.74 0.74 0.36
0.38 0.74 0.74 0.36
0.39 0.74 0.74 0.36
0.40 0.74 0.74 0.36
0.41 0.75 0.75 0.41
0.42 0.74 0.75 0.41
0.43 0.75 0.75 0.43
0.44 0.76 0.76 0.44
0.45 0.77 0.77 0.45
0.46 0.78 0.78 0.46
0.47 0.78 0.78 0.47
0.48 0.79 0.79 0.48
0.49 0.79 0.79 0.48
0.50 0.79 0.79 0.50
0.51 0.80 0.80 0.51
0.52 0.80 0.80 0.51
0.53 0.80 0.80 0.53
0.54 0.81 0.81 0.54
0.55 0.81 0.81 0.54
0.56 0.81 0.81 0.54
0.57 0.81 0.81 0.54
0.58 0.81 0.81 0.58
0.59 0.82 0.82 0.59
0.60 0.82 0.82 0.59
0.61 0.82 0.82 0.59
0.62 0.83 0.83 0.62
0.63 0.83 0.83 0.63
0.64 0.83 0.83 0.63
0.65 0.84 0.84 0.65
0.66 0.85 0.85 0.66
0.67 0.85 0.85 0.66
0.68 0.86 0.86 0.68
0.69 0.86 0.86 0.69
0.70 0.86 0.86 0.69
0.71 0.86 0.86 0.69
0.72 0.85 0.86 0.69
0.73 0.86 0.86 0.73
0.74 0.87 0.87 0.74
0.75 0.87 0.87 0.74
0.76 0.87 0.87 0.74
0.77 0.86 0.87 0.74
0.78 0.86 0.87 0.74
0.79 0.87 0.87 0.74
0.80 0.87 0.87 0.74
0.81 0.88 0.88 0.81
0.82 0.90 0.90 0.82
0.83 0.91 0.91 0.83
0.84 0.92 0.92 0.84
0.85 0.91 0.92 0.84
0.86 0.92 0.92 0.86
0.87 0.92 0.92 0.86
0.88 0.92 0.92 0.86
0.89 0.93 0.93 0.89
0.90 0.94 0.94 0.90
Final Results:
Best Precision: 0.94
Best Threshold: 0.90