Summary  
This chapter explains a unified class-interface design where objects implement specific methods—`.fit()` for learning (estimators), `.transform()` for data transformation (transformers), `.predict()` for output generation (predictors), and `.score()` for evaluation (models)—to enable consistent data processing and modeling workflows.

General domain of usage  
Machine learning preprocessing and modeling

The **scikit-learn** (`sklearn`) library provides tools for preprocessing and modeling. Its main object types are **estimator**, **transformer**, **predictor**, and **model**.

## Estimator

Any class with `.fit()` is an estimator — it **learns from data**.

```python
estimator.fit(X, y)     # supervised  
estimator.fit(X)        # unsupervised
```

## Transformer

A transformer has `.fit()` and `.transform()`, plus `.fit_transform()` to do both at once.

Transformers are usually used to transform the `X` array. However, as we will see in the example of `LabelEncoder`, some transformers are made for the `y` array.

Note

`nan` values shown in the training set in the picture indicate missing data in Python.

## Predictor

A predictor is an estimator with `.predict()` for generating outputs.

```python
predictor.fit(X, y)
predictor.predict(X_new)
```

## Model

A model is a predictor with `.score()`, which evaluates performance.

```python
model.fit(X, y)
model.score(X, y)
```

As mentioned in the previous chapter, **accuracy** is a metric representing the percentage of correct predictions.

The **preprocessing** stage involves working with transformers, and we work with predictors (more specifically with models) at the **modeling** stage.

Machine learning drives modern technological innovation across all industries. Embark on a comprehensive introduction to predictive modeling by mastering foundational algorithmic concepts utilizing Scikit-Learn. Participants will construct robust classifiers, evaluate performance metrics, ultimately culminating in the development of a complete predictive project.

Scikit-learn Concepts

Estimator

Transformer

Predictor

Model