Fine-tuning a machine learning model is part science, part experimentation. Here’s a clear, structured approach to know what adjustments to make for better performance:
1. Evaluate Model Performance First
Before adjusting anything, measure how well your model is performing:
Regression metrics (numeric target like spend):
Mean Absolute Error (MAE) — average absolute difference between predictions and actuals
Mean Squared Error (MSE) / Root MSE — penalizes larger errors more
R² (coefficient of determination) — proportion of variance explained
Classification metrics (categorical target):
Accuracy — % of correct predictions
Precision / Recall / F1 score — important if classes are imbalanced
Confusion matrix — see types of errors (false positives vs false negatives)
Use test or validation set to ensure evaluation reflects unseen data performance.
2. Adjust Model Parameters
Parameters you can tweak often depend on the model type:
Linear Regression
Learning rate (params.learning_rate): Smaller values make training more stable, but slower; larger values can overshoot optimal solutions.
Max iterations (params.max_iterations): Increase if the model hasn’t converged.
Logistic Regression
Regularization strength: Penalizes overly complex models to prevent overfitting.
Learning rate & iterations: Same as linear regression.
k-NN
k (number of neighbors):
Smaller k → sensitive to noise, may overfit
Larger k → smoother predictions, may underfit
Distance metric: Euclidean, Manhattan, etc. Can affect neighbor selection.
General Tips
Normalize features: Important for distance-based models like k-NN.
Encode categorical features properly: Linear/logistic models can’t use strings directly.
Handle missing values: Imputation usually improves model stability.
3. Feature Engineering
Sometimes tuning the model isn’t enough — the data itself is the key:
Add new features that capture relationships (e.g., income_per_age)
Remove irrelevant or noisy features to reduce overfitting
Transform skewed distributions (log, square root) for better modeling