Thread by @TedPetrou

There are many types of scikit-learn Estimators. The most common are:
Regressors
Classifiers
Clusterers
Transformers
Meta-estimators

Read their exact definition in the glossary scikit-learn.org/stable/glossar…

They all follow the same three-step process - import, instantiate, fit

Here are examples of each one. They all look the same

# Regressor
from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X, y)

# Classifier
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier()
dtc.fit(X, y)

# Clusterer
from sklearn.cluster import KMeans
km = KMeans()
km.fit(X)

# Transformer
from sklearn.preprocessing import StandardScaler
ss = StandardScaler()
ss.fit(X)

# Meta-estimators
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
lasso = Lasso()
grid = {'alpha': [.001, .01, .1, 1, 10]}
gs = GridSearchCV(lasso, grid)
gs.fit(X, y)