Using Sckit-Learn

1. Install scikit-learn

• conda install scikit-learn

2. General Form

• 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) 

3. Available in all Estimators

• 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))

4. Available in supervised estimators

• 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)
  

5. Available in unsupervised estimators

• 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