Scikit Learn Notes

sklearn Legend

import	task
sklearn.model_selection	Helps in selecting between models. Also, splitting train-test
sklearn.impute	Missing Values
sklearn.preprocessing	Change the form of data. Ordinal Categorical \(\to\) Numbers. Make it machine friendly
sklearn.base	Access base classes to create custom estimator, transformer
sklearn.pipeline	Create pipelines
sklearn.compose	Access to column transformers (take care of categorical and numerical columns in the same transformation)
sklearn.metrics	Score functions; Performance metrics; Distance computations
sklearn.tree	Decision tree based models
sklearn.ensemble	Various ensemble methods (e.g. RandomForestRegressor)
sklearn.svm	Dedicated for Support Vector Machines???
sklearn.multiclass	Access to OneVsRestClassifier and OneVsOneClassifier to override default multiclass behavior in Binary classifiers
sklearn.neighbors	Distance based models

Design Philosophy

Consistency

All objects share a consistent and simple interface

Object	Explanation	Example
Estimators	- Any object that can estimate some parameters based on the estimator - The estimation is done by the .fit() method. - Takes only a dataset as a parameter (or 2 (data and labels) for supervised learning algorithms) - Any other parameter is considered as a hyperparameter (e.g. strategy)	Imputer
Transformers	- Any object that can transform the dataset. - The transformation is performed by the .transform() method, which takes input as a dataset to be transformed - Both .fit() and .transform() can be conveniently be called together (with possible optimization) via the .fit_transform() function	Imputer
Predictors	- Capable of making predictions. - The .predict() method takes a dataset of new instances and returns a dataset of corresponding predictions. - A predictor also has a .score() method that measures the quality of predictions, given a test set (w/ labels, in case of supervised learning).

Having learnt the above, now try implementing an [[Imputing Data using Scikit-Learn|imputing strategy in Scikit-Learn]].

Inspection

1. Estimator's hyperparameters are accessible directly via public instance variables, e.g. imputer.strategy
2. Estimator's learned parameters are accessible in a similar fashion but with an _ (underscore) suffix, e.g. imputer.statistics_

Nonproliferation of Classes

1. All outputs given in the form of numpy arrays or scipy sparse matrices.
2. Hyperparameters are regular python string or numbers

Composition

Existing building blocks 1. Can be reused 2. Can be combined to create a Pipeline (arbitrary sequence of transformations followed by a final estimator)

Sensible defaults

1. Most common defaults
2. Baseline system is quick to create

Models

Classifiers

Model
sgd	Stochastic Gradient Descent	uses .decision_function()	Multiple
forest	Random Forest Classifier	uses .predict_proba()	Multiple
	Naive Bayes		Multiple
svc	Support Vector Machine Classifier	uses .decision_function()	Binary w/ OvO strategy

Attributes