`scikit-learn`

`conda install scikit-learn`

- every algorithm is exposed in scikit-learn via an 'Estimator'
- First you'll import the model, the general form is :

-`from sklearn.{family} import {Model}`

**Estimator parameters**: All the parameters of an estimator can be set when it is instantiated

- It will have suitable values`model = LinearRegression(normalize=True) print(model)`

`model.fit()`

: fit training data

- for supervised learning applications , this accepts two arguments : the data x and the labels y (e.g.`model.fit(x,y)`

)

- for unsupervised learning applications, this accepts only a single argument (e.g.`model.fit(x)`

)

`model.predict_proba()`

: returns the probability that a new observation has each categorical label. In this case, the label with the highest probability is returned by`model.predict()`

`model.score()`

: for classification or regression problems, scores are between 0 and 1 (larger score = better fit)

`model.predict()`

: predict labels in clustering algorithms`model.transform()`

: given an unsupervised model, transform new data into the new basis. This also accepts`x_new`

, returns the new representation of the data based on the unsuprvised model`model.fit_transform()`

: for some estimators. which efficiently performs a fit and transform on the same input data