Scikit-Learn Models
A practical guide to major models, their purpose, when to use them, when not to use them, and quick model-selection rules.
How to Choose a Machine Learning Model
There is no single “best” algorithm for every dataset. Model selection depends on the type of target, dataset size, number and type of features, noise, nonlinearity, interpretability, computation time, and the evaluation metric.
Practical rule: Start with a simple baseline, validate correctly, then compare a few appropriate models. Do not select a model only because it is more powerful.
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Regression Models
| Model | What it does | When to use | When NOT to use |
|---|---|---|---|
| Linear Regression | Predicts a continuous value using a linear relationship. | Relationship is approximately linear; excellent baseline. | Strongly nonlinear relationships; severe outliers. |
| Ridge Regression | Linear regression with L2 regularization. | Many features, multicollinearity, or overfitting in linear models. | When automatic feature removal is specifically required. |
| Lasso Regression | Linear regression with L1 regularization. | High-dimensional data where automatic feature selection is useful. | When many correlated features are all important; Lasso may select only some. |
| ElasticNet | Combines L1 and L2 regularization. | Correlated features plus a need for feature selection. | Very simple problems where ordinary regression is sufficient. |
Easy rule: Simple linear relationship → Linear Regression | Too many/correlated features → Ridge | Need feature selection → Lasso | Need both → ElasticNet
2
Classification Models
| Model | What it does | When to use | When NOT to use |
|---|---|---|---|
| Logistic Regression | Predicts class probabilities. | Binary/multiclass classification; strong baseline; interpretable model. | Highly nonlinear boundaries without feature engineering. |
| Linear Discriminant Analysis (LDA) | Finds a linear boundary between classes using class distributions. | Small/medium datasets; classes reasonably Gaussian with similar covariance. | Strongly nonlinear boundaries or very different covariance structures. |
| Gaussian Naive Bayes | Uses Bayes' theorem with a Gaussian assumption for features. | Fast classification; continuous features; small datasets. | Features have complex dependencies that strongly violate the assumption. |
Important: For text classification, Multinomial Naive Bayes is usually more natural for word counts or TF-IDF-like nonnegative features. GaussianNB is intended for continuous features that can be modeled approximately by Gaussian distributions.
3
Ensemble Methods
| Model | What it does | When to use | When NOT to use |
|---|---|---|---|
| Random Forest | Combines many decision trees. | Tabular data; nonlinear relationships; robust general-purpose model. | Need a very small/interpretable model or strong extrapolation. |
| Gradient Boosting | Builds trees sequentially, correcting previous errors. | High predictive performance on tabular data. | Extremely large datasets when training time is a major concern. |
| AdaBoost | Gives greater emphasis to incorrectly classified observations. | Relatively clean classification data with simple weak learners. | Very noisy data or substantial outliers can cause problems. |
Random Forest
Many trees are built independently and their predictions are combined.
Gradient Boosting
Trees are built sequentially; each new tree attempts to correct previous errors.
AdaBoost
Later weak learners focus more strongly on observations that earlier learners handled poorly.
Practical tip: Random Forest is often an excellent first choice for structured/tabular data. Gradient Boosting can be a strong choice when you want to push predictive performance.
4
Clustering
| Model | What it does | When to use | When NOT to use |
|---|---|---|---|
| K-Means | Divides observations into K groups. | Compact, roughly spherical clusters; K can be specified. | Irregular shapes, substantial noise, or unknown number of clusters. |
| DBSCAN | Finds dense regions and identifies noise. | Arbitrary-shaped clusters; outlier detection; number of clusters unknown. | Clusters have very different densities or parameter selection is difficult. |
Example: Customer Segmentation
Suppose customer data contains Age, Income, and Annual Spending, but no customer-type label.
Customer Data
↓
K-Means
↓
Cluster 1 → Low spending
Cluster 2 → Medium spending
Cluster 3 → High spending
5
Dimensionality Reduction
| Model | What it does | When to use | When NOT to use |
|---|---|---|---|
| PCA | Converts many correlated features into fewer components. | Reduce dimensionality; preprocessing; visualization; remove redundancy. | When original feature interpretability is essential. |
| t-SNE | Creates a 2D/3D representation emphasizing local neighborhoods. | Visual exploration of high-dimensional data. | As a general preprocessing method or when reliable global distances are required. |
PCA — Preprocessing
100 features
↓
PCA
↓
10 components
↓
ML Model
PCA can be used as a preprocessing technique.
t-SNE — Visualization
100 features
↓
t-SNE
↓
2D plot
↓
Explore patterns
t-SNE is primarily an exploratory visualization technique.
6
Neural Networks
| Model | What it does | When to use | When NOT to use |
|---|---|---|---|
| MLP (Multi-Layer Perceptron) | Neural network for classification and regression. | Nonlinear relationships; medium-sized tabular datasets; neural-network approach. | Very small datasets; interpretability is important; specialized image/text deep-learning tasks. |
MLP Structure
Input Features
↓
Input Layer
↓
Hidden Layer 1
↓
Hidden Layer 2
↓
Output Layer
↓
Prediction
MLPClassifier → Classification | MLPRegressor → Regression
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Complete Quick Selection Guide
| Your Problem | Start With |
|---|---|
| Simple numerical prediction | Linear Regression |
| Linear regression + overfitting | Ridge |
| Need automatic feature selection | Lasso |
| Need L1 + L2 regularization | ElasticNet |
| Binary classification | Logistic Regression |
| Linear classification with distribution assumptions | LDA |
| Fast probabilistic classification | Naive Bayes |
| General-purpose tabular ML | Random Forest |
| High-performance tabular ML | Gradient Boosting |
| Boosting with simple weak learners | AdaBoost |
| Known number of compact clusters | K-Means |
| Irregular clusters + noise | DBSCAN |
| Reduce features | PCA |
| Visualize high-dimensional data | t-SNE |
| Nonlinear neural-network prediction | MLP |
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One-Line Memory Trick
REGRESSION
Linear → Ridge → Lasso → ElasticNet
CLASSIFICATION
Logistic → LDA → Naive Bayes
ENSEMBLE
Random Forest → Gradient Boosting → AdaBoost
CLUSTERING
K-Means → DBSCAN
DIMENSION REDUCTION
PCA → t-SNE
NEURAL NETWORK
MLP
Final Practical Advice
Model selection should be based on experimentation and validation. Consider dataset size, feature types, scaling requirements, class imbalance, missing values, noise, interpretability, training time, and the correct evaluation metric.
Start Simple
Build a baseline before moving to complex models.
Validate Correctly
Use appropriate train/test splits or cross-validation.
Compare Models
Evaluate suitable alternatives using the same validation strategy.
Avoid Leakage
Keep preprocessing inside a proper pipeline whenever appropriate.
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