Summary  
Scikit-learn organizes its components into four class types—estimators, transformers, predictors, and models—each defined by specific methods (.fit, .transform, .predict, .score) to build and evaluate data pipelines.

General domain of usage  
Machine learning (data preprocessing and predictive 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 is now used everywhere. Want to learn it yourself? This course is an introduction to the world of Machine learning for you to learn basic concepts, work with Scikit-learn – the most popular library for ML and build your first Machine Learning project.
This course is intended for students with a basic knowledge of Python, Pandas, and Numpy.

Learn the Machine Learning concepts and the ML project workflow.

Preprocessing is probably the most important stage of an ML project. This chapter covers the preprocessing steps needed for almost any dataset.

A pipeline is a neat way to combine all the preprocessing steps as well as a model. Pipelines make it much easier to train and use a model.

Modeling is the most fun stage of an ML project. Let's learn to build, fine-tune and evaluate the model!

Scikit-learn Concepts

Estimator

Transformer

Predictor

Model

Scikit-learn Concepts

Estimator

Transformer

Predictor

Model