Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Mastering Machine Learning with scikit-learn
  • Table Of Contents Toc
  • Feedback & Rating feedback
Mastering Machine Learning with scikit-learn

Mastering Machine Learning with scikit-learn

By : Gavin Hackeling
5 (2)
close
close
Mastering Machine Learning with scikit-learn

Mastering Machine Learning with scikit-learn

5 (2)
By: Gavin Hackeling

Overview of this book

Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance. By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.
Table of Contents (15 chapters)
close
close
9
From Decision Trees to Random Forests and Other Ensemble Methods

Installing scikit-learn

This book was written for version 0.18.1 of scikit-learn; use this version to ensure that the examples run correctly. If you have previously installed scikit-learn, you can retrieve the version number by executing the following in a notebook or Python interpreter:

# In[1]:
import sklearn
sklearn.__version__

# Out[1]:
'0.18.1'
The package is named sklearn because scikit-learn is not a valid Python package name.

If you have not previously installed scikit-learn, you may install it from a package manager or build it from source. We will review the installation processes for Ubuntu 16.04, Max OS, and Windows 10 in the following sections, but refer to http://scikit-learn.org/stable/install.html for the latest instructions. The following instructions assume only that you have installed Python >= 2.6 or Python >= 3.3. See http://www.python.org/download/ for instructions on installing Python.

Installing using pip

The easiest way to install scikit-learn is to use pip, the PyPA-recommended tool for installing Python packages. Install scikit-learn using pip as follows:

$ pip install -U scikit-learn

If pip is not available on your system, consult the following sections for installation instructions for various platforms.

Installing on Windows

scikit-learn requires setuptools, a third-party package that supports packaging and installing software for Python. Setuptools can be installed on Windows by running the bootstrap script at https://bitbucket.org/pypa/setuptools/raw/bootstrap/ez_setup.py.

Windows binaries for the 32-bit and 64-bit versions of scikit-learn are also available. If you cannot determine which version you need, install the 32-bit version. Both versions depend on NumPy 1.3 or newer. The 32-bit version of NumPy can be downloaded from http://sourceforge.net/projects/numpy/files/NumPy/. The 64-bit version can be downloaded from http://www.lfd.uci.edu/~gohlke/pythonlibs/#scikit-learn.

A Windows installer for the 32-bit version of scikit-learn can be downloaded from http://sourceforge.net/projects/scikit-learn/files/. An installer for the 64-bit version of scikit-learn can be downloaded from http://www.lfd.uci.edu/~gohlke/pythonlibs/#scikit-learn.

Installing on Ubuntu 16.04

scikit-learn can be installed on Ubuntu 16.04 using apt.

$ sudo apt install python-scikits-learn

Installing on Mac OS

scikit-learn can be installed on OS X using Macports.

$ sudo port install py27-sklearn

Installing Anaconda

Anaconda is a free collection of more than 720 open source data science packages for Python including scikit-learn, NumPy, SciPy, pandas, and matplotlib. Anaconda is platform-agnostic and simple to install. See https://docs.continuum.io/anaconda/install/ for instructions for your operating system.

Verifying the installation

To verify that scikit-learn has been installed correctly, open a Python console and execute the following:

# In[1]:
import sklearn
sklearn.__version__

# Out[1]:
'0.18.1'

To run scikit-learn's unit tests, first install the nose Python library. Then execute the following in a terminal emulator:

$ nosetest sklearn -exe  

Congratulations! You've successfully installed scikit-learn.

Create a Note

Modal Close icon
You need to login to use this feature.
notes
bookmark search playlist font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Edit Note

Modal Close icon
Write a note (max 255 characters)
Cancel
Update Note

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY