Learn how to select the right model, train it using proper techniques, evaluate its performance with key metrics, and improve accuracy.
The first step in modeling is choosing the right type of model for your problem. Models are broadly categorized as Predictive or Descriptive.
Use supervised learning ā learn from labeled data to predict outcomes for new, unseen data.
Use unsupervised learning ā discover hidden patterns and structures in data without any target.
Training means feeding data to an algorithm so it can learn patterns. The critical question is: how do we split data for training and testing?
The simplest approach: split the dataset into two parts.
Holdout Split Example (80/20)
Limitation: Results depend heavily on which data ends up in training vs test. A single unlucky split can give misleading results ā high variance in performance estimates.
A more robust technique that uses all data for both training and testing across multiple rounds:
| Aspect | Holdout | K-Fold Cross-Validation |
|---|---|---|
| Number of splits | 1 | K |
| Data usage | Part of data never used for training | All data used for both training and testing |
| Variance | High (depends on the split) | Low (averaged over K runs) |
| Computation | Fast | K times slower |
| Typical K values | N/A | 5 or 10 |
| Best for | Large datasets, quick prototyping | Small datasets, robust evaluation |
Interpretability refers to how easily humans can understand and explain a model's predictions. This is crucial in high-stakes domains like healthcare, finance, and criminal justice.
Accuracy vs Interpretability Trade-off: More complex models (neural networks) often achieve higher accuracy but are harder to interpret. Simpler models (linear regression) are easily explainable but may miss complex patterns. Choose based on your domain requirements.
A Confusion Matrix is a table that summarizes the performance of a classification model by comparing actual vs predicted labels.
| Predicted Positive | Predicted Negative | |
|---|---|---|
| Actual Positive | TP (True
Positive) Correctly predicted positive |
FN (False
Negative) Missed ā Type II Error |
| Actual Negative | FP (False
Positive) False alarm ā Type I Error |
TN (True
Negative) Correctly predicted negative |
Overall correctness ā what fraction of all predictions were right.
Of all predicted positives, how many were actually positive? High precision = low false alarm rate.
Of all actual positives, how many did the model catch? High recall = missing few positives.
Harmonic mean of precision and recall ā a balanced measure when you need both.
When to prioritize which metric?
ā¢ Precision when false positives are costly (spam filter ā don't want
to block real emails).
ā¢ Recall when false negatives are costly (disease detection ā don't
want to miss a sick patient).
ā¢ F1 when you need a balance between the two.
Enter your confusion matrix values and see all metrics computed in real-time:
| Predicted Positive | Predicted Negative | |
|---|---|---|
| Actual Positive | TP | FN |
| Actual Negative | FP | TN |
If your model doesn't perform well enough, here are proven strategies to improve it:
Create informative features, combine existing ones, or apply domain knowledge to extract better inputs for the model.
Adjust model settings (learning rate, tree depth, K value) using Grid Search or Random Search.
L1 (Lasso) or L2 (Ridge) regularization penalizes overly complex models to prevent overfitting.
Use K-fold cross-validation to get a reliable estimate and avoid misleading results from a single split.
Use SMOTE (oversampling), undersampling, or class weights to address class imbalance.
More training data helps the model learn more diverse patterns and improve generalization.
Adjust model complexity and dataset size to see how they affect training vs testing accuracy.