# Fitting and prediction: estimator basics#

ruptures has an object-oriented modelling approach (largely inspired by scikit-learn): change point detection algorithms are broken down into two conceptual objects that inherits from base classes: BaseEstimator and BaseCost.

## Initializing a new estimator#

Each change point detection algorithm inherits from the base class ruptures.base.BaseEstimator. When a class that inherits from the base estimator is created, the .__init__() method initializes an estimator with the following arguments:

• model: "l1", "l2", "normal", "rbf", "linear", etc. Cost function to use to compute the approximation error.
• cost: a custom cost function to the detection algorithm. Should be a BaseCost instance.
• jump: reduce the set of possible change point indexes; predicted change points can only be a multiple of jump.
• min_size: minimum number of samples between two change points.

## Making a prediction#

The main methods are .fit(), .predict(), .fit_predict():

• .fit(): generally takes a signal as input and fit the algorithm to the data.
• .predict(): performs the change point detection. This method returns a list of indexes corresponding to the end of each regimes. By design, the last element of this list is the number of samples.
• .fit_predict(): helper method which calls .fit() and .predict() successively.