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)

Grid search with scikit-learn

At the beginning of the model selection and cross-validation chapter we tried to select the best nearest-neighbor model for the two last features of the iris dataset. We will refocus on that now with GridSearchCV in scikit-learn.

Getting ready

First, load the last two features of the iris dataset. Split the data into training and testing sets:

from sklearn import datasets

iris = datasets.load_iris()
X = iris.data[:,2:]
y = iris.target

from sklearn.model_selection import train_test_split, cross_val_score

X_train, X_test, y_train, y_test = train_test_split(X, y, stratify = y,random_state = 7)

How to do it...