Book Image

scikit-learn Cookbook - Second Edition

By : Trent Hauck
Book Image

scikit-learn Cookbook - Second Edition

By: Trent Hauck

Overview of this book

Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. This book includes walk throughs and solutions to the common as well as the not-so-common problems in machine learning, and how scikit-learn can be leveraged to perform various machine learning tasks effectively. The second edition begins with taking you through recipes on evaluating the statistical properties of data and generates synthetic data for machine learning modelling. As you progress through the chapters, you will comes across recipes that will teach you to implement techniques like data pre-processing, linear regression, logistic regression, K-NN, Naïve Bayes, classification, decision trees, Ensembles and much more. Furthermore, you’ll learn to optimize your models with multi-class classification, cross validation, model evaluation and dive deeper in to implementing deep learning with scikit-learn. Along with covering the enhanced features on model section, API and new features like classifiers, regressors and estimators the book also contains recipes on evaluating and fine-tuning the performance of your model. By the end of this book, you will have explored plethora of features offered by scikit-learn for Python to solve any machine learning problem you come across.
Table of Contents (13 chapters)

Create a simple estimator

We are going to do some work towards building our own scikit-learn estimator. The custom scikit-learn estimator consists of at least three methods:

  • An __init__ initialization method: This method takes as input the estimator's parameters
  • A fit method: This trains the estimator
  • A predict method: This method performs a prediction on unseen data

Schematically, the class looks like this:

#Inherit from the classes BaseEstimator, ClassifierMixin
class RidgeClassifier(BaseEstimator, ClassifierMixin):

def __init__(self,param1,param2):
self.param1 = param1
self.param2 = param2

def fit(self, X, y = None):
#do as much work as possible in this method
return self

def predict(self, X_test):
#do some work here and return the predictions, y_pred
return y_pred
...