Scikit Learn Image Classification Example

This will be a tutorial-style talk demonstrating how to use pandas and scikit-learn to do classification tasks. We will learn Classification algorithms, types of classification algorithms, support vector machines(SVM), Naive Bayes, Decision Tree and Random Forest Classifier in this tutorial. Associated Scikit Links. A comprehensive summary of feature extraction techniques for images is well beyond the scope of this section, but you can find excellent implementations of many of the standard approaches in the Scikit-Image project. Ensembles can give you a boost in accuracy on your dataset. , & Russell, C. It will cover basic classification and regression problems, regularization of learning models, basic cross-validation, and some examples from text mining and image processing, all using the tools available in scikit-learn. SVC, which implements support vector classification. The general outline of the talk is reported as follows (the order of the topics may vary): * Intro to Machine Learning * Machine Learning in Python * Intro to Scikit-Learn * Overview of Scikit-Learn. This Estimator executes an Scikit-learn script in a managed Scikit-learn execution environment, within a SageMaker Training Job. The second to eight lines of code instantiates three models - Logistic Regression, Decision Tree, and Support Vector Machine - and appends these algorithms into an object ‘estimator’. If your images of different sizes, most trivial (but certainly not the best) thing that you can do is pad all images to the size of largest image with, for example, white pixels. *FREE* shipping on qualifying offers. This project is predicting income with random forest models using scikit learn. Since this is a very simplistic dataset with distinctly separable classes. Along with covering the enhanced features on model section, API and new features like classifiers, regressors and estimators the book also contains recipes on. Packages and applications¶. If you plan to work with neural networks and Python, you'll need Scikit-learn. , & Russell, C. (Stay tuned, as I keep updating the post while I grow and plow in my deep learning garden:). A comprehensive summary of feature extraction techniques for images is well beyond the scope of this section, but you can find excellent implementations of many of the standard approaches in the Scikit-Image project. It use the plot and display methods from the class Output. cluster import KMeans # Specify the number of clusters (3) and fit the data X kmeans = KMeans(n_clusters=3, random_state=0). Although this example uses Scikit-Learn’s SGDClassifer, the Incremental meta-estimator will work for any class that implements partial_fit and the scikit-learn base estimator API. This example is taken from the Python course "Python Text Processing Course" by Bodenseo. March 2015. Step 2: Loading the data set in jupyter. Apart from the well-optimized ML routines and pipeline building methods, it also boasts of a solid collection of utility methods for synthetic data. Scikit learn offers a very wide rang of clasifiers with different characteristics. Here, the authors show incorporating environment-specific. Clustering. The ability of a machine learning model to classify or label an image into its respective class with the help of learned features from hundreds of images is called as Image Classification. Import statements and additional comments are ignored in this blogpost but you can found them in examples folder. Since this is a very simplistic dataset with distinctly separable classes. For example, neural networks was added in the latest release. Example: “ I have. scikit-learn 0. This stuff is useful in the real-world. Collection of machine learning algorithms and tools in Python. For instance, we are not going to put image processing in scikit-learn. An example showing how the scikit-learn can be used to recognize images of hand-written digits. 1 of An Introduction to Statistical Learning (11 pages) and related videos: K-fold and leave-one-out cross-validation (14 minutes), Cross-validation the right and wrong ways (10 minutes). The library supports state-of-the-art algorithms such as KNN, XGBoost, random forest, SVM among others. read_data_sets ("MNIST_data/") mnist_images = mnist. The book combines an introduction to some of the main concepts and methods in machine learning with practical, hands-on examples of real-world problems. Pipelining: chaining a PCA and a logistic regression The PCA does an unsupervised dimensionality reduction, while the logistic regression does the p. In this tutorial, I will discuss about how to perform texture matching using Local Binary Patterns (LBP). Cross decomposition; Dataset examples. 3 Other versions. For the __init__ class contructor parameters: see the sklearn. Simple Example of Linear Regression With scikit-learn in Python; K-Fold Cross Validation; Detect number of faces from an image in Python using OpenCV; Image Classification in Python using CNN; using sklearn StandardScaler() to transform input dataset values. It return the 2D array of arrays with 3 values in each array, giving coordinates and std. Svm classifier mostly used in addressing multi-classification problems. The tutorial will guide you through the process of implementing linear regression with gradient descent in Python, from the ground up. Should know how to extract certain. This tutorial is the second of a two-part series. An example of an estimator is the class sklearn. The situation is as follow: 1. We build and train a DT classifier on the cancer dataset. We are going to classify a multitemporal image stack of MODIS NDVI time series (MOD13Q1). Step 2: Loading the data set in jupyter. In the model the building part, you can use the cancer dataset, which is a very famous multi-class classification problem. python, machine-learning, scikit-learn, svm, libsvm, I am using scikit-learn library to perform a supervised classification (Support Vector Machine classifier) on a satellite image. 10 comments. Example 3: The third example is Software, which shows how you will look when you get older. This example is commented in the tutorial section of the user manual. Image classification is a classical image recognition problem in which the task is to assign labels to images based their content or metadata. How were the results? Did the machine predict correctly? My output testing the [-5] example. Each example is a 28x28 pixel image flattened in an array with 784 values representing each pixel's intensity. The advantages of support vector machines are: Effective in high dimensional spaces. Presorting the feature over all relevant samples,. It will cover basic classification and regression problems, regularization of learning models, basic cross-validation, and some examples from text mining and image processing, all using the tools available in scikit-learn. Calibration. KNN (k-nearest neighbors) classification example¶ The K-Nearest-Neighbors algorithm is used below as a classification tool. Scikit-learn in NNI 2. Example: “ I have. Svm classifier mostly used in addressing multi-classification problems. This stuff is useful in the real-world. Scikit-learn is widely used in kaggle competition as well as prominent tech companies. This is a classic case of multi-class classification problem, as the number of species to be predicted is more than two. TensorFlow examples (text-based) This page provides links to text-based examples (including code and tutorial for most examples) using TensorFlow. The ability of a machine learning model to classify or label an image into its respective class with the help of learned features from hundreds of images is called as Image Classification. You can vote up the examples you like or vote down the ones you don't like. Covariance estimation. In example from above, every pixel is represent a feature. First Steps of Learning Deep Learning: Image Classification in Keras for example tree-based such as random How to Extend Scikit-learn and Bring Sanity to Your. EMBED (for wordpress. By swapping out a single class import, users can distribute cross-validation for their existing scikit-learn workflows. We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers. Congratulations, you have reached the end of this scikit-learn tutorial, which was meant to introduce you to Python machine learning! Now it's your turn. 1 classification¶ This example uses the dataset of digits, which is made up of 1797 8x8 images, and each image is a hand-written digit, the. TensorFlow examples (text-based) This page provides links to text-based examples (including code and tutorial for most examples) using TensorFlow. For that reason, I think the best place to start with Scikit-Learn is the Choosing the right estimator page. If your images of different sizes, most trivial (but certainly not the best) thing that you can do is pad all images to the size of largest image with, for example, white pixels. This documentation is for scikit-learn version 0. Document Classification with scikit-learn (Github, tutorial) Scikit-learn tutorial at SciPy2016; Clustering text documents using k-means; Working With Text Data; Classification of text documents using sparse features; Latent Dirichlet Allocation with online variational Bayes algorithm. Generalized. My friend's one-versus-one MATLAB implementation correctly classified 19 of 22 feature vectors. Scikit-Learn Cheat Sheet. These examples require some basic knowledge of image processing. It needs to be in a package that is specialized for this data. Tutorial: image classification with scikit-learn In this tutorial we will set up a machine learning pipeline in scikit-learn, to preprocess data and train a model. We are going to classify a multitemporal image stack of MODIS NDVI time series (MOD13Q1). Scikit learn is a library used to perform machine learning in Python. KNN Classification using Scikit-learn Learn K-Nearest Neighbor(KNN) Classification and build KNN classifier using Python Scikit-learn package. Apart from the well-optimized ML routines and pipeline building methods, it also boasts of a solid collection of utility methods for synthetic data. Th classifier is trained on the NPS Chat Corpus which consists of over 10,000 posts from instant. scikit-learn user guide Release 0. The hotter the pixel, the more important. KNN used in the variety of applications such as finance, healthcare, political science, handwriting detection, image recognition and video recognition. So eliminating elements from that array by resizing it,. If you use the software, please consider citing scikit-learn. Should know how to extract certain. This tutorial shows how to use Azure Machine Learning to deploy an image classification model with scikit-learn in a Python Jupyter notebook. Examples based on real world datasets. Back in April, I provided a worked example of a real-world linear regression problem using R. This is a subreddit for the discussion of statistical theory, software and application. Simple visualization and classification of the digits dataset¶. In this section and the ones that follow, we will be taking a closer look at several specific algorithms for supervised and unsupervised learning, starting here with naive Bayes classification. Asirra dataset classification using transformer¶ This example shows a basic use of the OverfeatTransformer in a scikit-learn pipeline in order to do classification of natural images. To train the random forest classifier we are going to use the below random_forest_classifier function. Input and output data are labeled for classification to provide a learning basis for future data prediction. We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers. The library supports state-of-the-art algorithms such as KNN, XGBoost, random forest, SVM among others. Conclusion. They are targeted at existing or would-be scikit-image developers wishing to develop their knowledge of image processing algorithms. Most of the classification examples start with iris dataset, so let's pick another dataset within scikit-learn for this workflow. It features various. The first image belongs to class A with a probability of 70%, class B with 10%, C with 5% and D with 15% etc. 3 Reusable science scikit-learn is the new machine-learning textbook nilearn is the new neuroimaging review article. images, faces. The classification tools identify the category associated with provided data. classify. You will learn to incorporate machine learning in your applications. In this video we will learn how to recognize handwritten digits in python using machine learning library called scikit learn. Here are the main ways the Scikit-learn library is used. 12 min read. That's how to implement multi-class classification with logistic regression using scikit-learn. support vector machine, random forest, neural network, etc. The mathematicl equation for linear regression is. Classification. 11-git — Other versions. Multiclass classification using scikit-learn Multiclass classification is a popular problem in supervised machine learning. September 2016. In scikit-learn, an estimator for classification is a Python object that implements the methods fit(X,y) and predict(T). Interactive example So, let’s see Facets Dive in action, on a simple machine learning experiment. The library supports state-of-the-art algorithms such as KNN, XGBoost, random forest, SVM among others. ML is similar to AI up to some extent, however, there is a difference between the two. images mnist_labels = mnist. Scikit-learn is an amazing machine learning library that provides easy and consistent interfaces to many of the most popular machine learning algorithms. In this machine learning video we learn some basics of decision trees in scikit-learn. With Learning scikit-learn: Machine Learning in Python, you will learn to incorporate machine learning in your applications. Scikit-learn: Builds on top of Numpy and Scipy to provide machine learning algorithms like regression, classification, clustering etc. TensorFlow : Open-source library developed by Google to train deep neural networks. Face classification using Haar-like feature descriptor¶. images, faces. Haar-like feature descriptors were successfully used to implement the first real-time face detector 1. 如果你要使用软件,请考虑 引用scikit-learn和Jiancheng Li. We, the scikit-learn team, want to make plugin pandas into scikit-learn easier. Text Classification with Pandas & Scikit In this tutorial, we introduce one of most common NLP and Text Mining tasks, that of Document Classification. Certain decision tree based algorithms in Scikit-Learn are naturally able to handle multi-label classification. The code below also illustrates how the construction and the computation of the predictions can be parallelized within multiple jobs. This image processing also uses machine learning. \(prediction = bias + feature_1 contribution + … + feature_n contribution\). Scikit-learn is a popular machine learning library for Python and supports several operations natively like classification, regression, clustering and includes a wide variety such as DBSCAN and gradient boosting. After all the work we spent cleaning and formatting the data, actually creating, training, and predicting with the models is relatively simple. Biclustering. This module introduces basic machine learning concepts, tasks, and workflow using an example classification problem based on the K-nearest neighbors method, and implemented using the scikit-learn library. The most applicable machine learning algorithm for our problem is Linear SVC. Before hopping into Linear SVC with our data, we're going to show a very simple example that should help solidify your understanding of working with Linear SVC. Restrictions. Simple and efficient tools for data mining and data analysis; Accessible to everybody, and reusable in various contexts. Clustering. Example of logistic regression in Python using scikit-learn. The stack consists of 23 bands (16-day composites) with a spatial resolution of 231m in sinusoidal projection. My main issue is how to train my SVM classifier. An example of an estimator is the class sklearn. Scikit-learn offers an extensive range of built-in algorithms that make the most of data science projects. 这个文档适用于 scikit-learn 版本 0. In scikit-learn, an estimator for classification is a Python object that implements the methods fit(X, y) and predict(T). Classifier comparison¶ A comparison of a several classifiers in scikit-learn on synthetic datasets. Scikit-Learn Cheat Sheet. Pandas provides a Python library such as IPython toolkit and other libraries, the environment for doing data analysis in Python. Ensemble methods. Pixel importances with a parallel forest of trees This example shows the use of forests of trees to evaluate the importance of the pixels in an image classification task (faces). After all the work we spent cleaning and formatting the data, actually creating, training, and predicting with the models is relatively simple. Filed Under: Machine Learning Tagged With: classification, Grid Search, Kernel Trick, Parameter Tuning, Python, scikit-learn, Support Vector Machine, SVM. We will learn Classification algorithms, types of classification algorithms, support vector machines(SVM), Naive Bayes, Decision Tree and Random Forest Classifier in this tutorial. The scikit-learn API combines a user-friendly interface with a highly optimized implementation of several classification algorithms. scikit-image is a collection of algorithms for image processing. Welcome to lesson eight ‘Machine Learning with Scikit-Learn’ of the Data Science with Python Tutorial, which is a part of the Data Science with Python Course. Svm classifier mostly used in addressing multi-classification problems. We’ll be doing something similar to it, while taking more detailed look at classifier weights and predictions. com/gehlg/v5a. This Edureka video on "Scikit-learn Tutorial" introduces you to machine learning in Python. They are extracted from open source Python projects. It return the 2D array of arrays with 3 values in each array, giving coordinates and std. Haar-like feature descriptors were successfully used to implement the first real-time face detector 1. SVM Image Classification We are a looking for enthusiastic Machine learning and Deep learning developer who is good at writing algorithms, image processing and classifications. Simple and efficient tools for data mining and data analysis; Accessible to everybody, and reusable in various contexts. Image Analysis and Text Classification using CNNs in PyTorch Learn to Build Powerful Image and Document Classifiers in Minutes. The project is using the RandomForestClassifier, fits different forest models and makes predictions. Image classification with Python. Confusion Matrix¶. In classification, there’s generally an image with a single object as the focus and the task is to say what that image is (see above). Gaussian Process for Machine Learning. Covariance estimation. Ranging from handwritten digit recognition to document classification, examples are solved step by step using Scikit-learn and Python. Scikit Learn Multiclass Learning Data Talks. One of the most amazing things about Python's scikit-learn library is that is has a 4-step modeling pattern that makes it easy to code a machine learning classifier. The retrain script is the core component of our algorithm and of any custom image classification task that uses Transfer Learning from Inception v3. Computer Vision for Predicting Facial Attractiveness. Classification Test Problems. July 14-20th, 2014: international sprint. load_svmlight_file for the svmlight or libSVM sparse format; scikit-learn's datasets. If you are not aware of the multi-classification problem below are examples of multi-classification problems. Scikit-learn (formerly scikits. My main issue is how to train my SVM classifier. SVC that implements Support Vector Classification. This tutorial shows how to use Azure Machine Learning to deploy an image classification model with scikit-learn in a Python Jupyter notebook. The book combines an introduction to some of the main concepts and methods in machine learning with practical, hands-on examples of real-world problems. Classification - Machine Learning. The class is intrumented to be use with the scikit-learn cross validation. support vector machine, random forest, neural network, etc. Support vector machine classifier is one of the most popular machine learning classification algorithm. Scikit Learn provides an abundance of example use cases on its own website, which I found particularly useful when I first started playing with the library. 如果你要使用软件,请考虑 引用scikit-learn和Jiancheng Li. Official scikit-learn documentation. Note the LR model is present in the linear_model package of scikit-learn. The scikit-learn provides an object that, given data, computes the score during the fit of an estimator on a parameter grid and chooses the parameters to maximize the cross-validation score. Scikit-learn examples (image based) [Paper published] Check out our new paper for image classification with great results using only small sets of training data. Self-driving Cars — OpenCV and SVM Machine Learning with Scikit-Learn for Vehicle Detection on the Road Computer vision with OpenCV Machine Learning with Scikit-Learn. If you want to download the tra. Tutorial exercises. Next, start your own digit recognition project with different data. In this book, you will learn several methods for building Machine Learning applications that solve different real-world tasks, from document classification to image recognition. It is a practical, instructional book for hands-on machine learning and deep learning (a class of machine learning algorithms). Scikit-learn examples (image based) [Paper published] Check out our new paper for image classification with great results using only small sets of training data. Generalized. Examples based on real world datasets. scikit-learn v0. scikit-image is a Python package dedicated to image processing, and using natively NumPy arrays as image objects. Ranging from handwritten digit recognition to document classification, examples are solved step-by-step using scikit-learn and Python. Classification. After a short introduction to machine learning, you will learn: the principles of supervised machine learning for classification, how to install the whole scientific python suite,. An example showing how the scikit-learn can be used to recognize images of hand-written digits. especially as many datasets increasingly image vegetation and other subtle features in fine detail. I am working on classification problem and calculating accuracy and Jaccard score with scikit-learn which, I think, is a widely used library in pythonic scientific world. scikit-learn 0. With Learning scikit-learn: Machine Learning in Python, you will learn to incorporate machine learning in your applications. We, the scikit-learn team, want to make plugin pandas into scikit-learn easier. After training a model with logistic regression, it can be used to predict an image label (labels 0-9) given an image. Machine learning: the problem setting; Loading an example dataset; Learning and predicting; Model persistence; A tutorial on statistical-learning for scientific data processing. For example, neural networks was added in the latest release. import numpy as np from sklearn. Should know how to extract certain. Covariance estimation. scikit-learn’s datasets. Implementing Machine Learning Models in Scikit-Learn. Scikit-Learn Cheat Sheet. Feature Selection. Scikit-learn library will be used for machine-learning algorithms. In this folder, we have examples for advanced topics, including detailed explanations of the inner workings of certain algorithms. It starts with a didactic but lengthy way of doing things, and finishes with the idiomatic approach to pipelining in scikit-learn. ndarray and convertible to that by numpy. 11-git — Other versions. It was designed by TensorFlow authors themselves for this specific purpose (custom image classification). We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers. KNN (k-nearest neighbors) classification example¶ The K-Nearest-Neighbors algorithm is used below as a classification tool. Filed Under: Machine Learning Tagged With: classification, Grid Search, Kernel Trick, Parameter Tuning, Python, scikit-learn, Support Vector Machine, SVM. 18) now has built in support for Neural Network models! In this article we will learn how Neural Networks work and how to implement them with the Python programming language and the latest version of SciKit-Learn!. Description of the example 2. Cross decomposition; Dataset examples. First approach to classification. 1 — Other versions. In scikit-learn, an estimator is just a plain Python class that implements the methods fit(X, Y) and predict(T). Classification using K-Nearest Neighbor Classifier with Scikit Learn February 23, 2018 K-NN is a very simple machine learning algorithm which can classify a point based on other nearest points. SVM Image Classification We are a looking for enthusiastic Machine learning and Deep learning developer who is good at writing algorithms, image processing and classifications. *FREE* shipping on qualifying offers. • Trained 3 recognition systems on MNIST dataset (7, 291 labeled examples), achieved accuracy of 97. Since this is a very simplistic dataset with distinctly separable classes. We will be using scikit-learn (python) libraries for our example. Now you can load data, organize data, train, predict, and evaluate machine learning classifiers in Python using Scikit-learn. Check out below for an example for the iris dataset. The code demonstrates the use of pipelines,. gaussian_process module. In this tutorial, we will walk through a few of the classifications metrics in Python’s scikit-learn and write our own functions from scratch to understand the math behind a few of them. Importing trained scikit-learn models into Watson Machine Learning. If you have a scikit-learn model that you trained outside of IBM Watson Machine Learning, this topic describes how to import that model into your Watson Machine Learning service. from scikit-image. In this tutorial, we won't use scikit. They are extracted from open source Python projects. asarray) and sparse (any scipy. 0 is available for download. Data Science in Python, Pandas, Scikit-learn, Numpy, Matplotlib; Conclusion. scikit-learn’s datasets. Multi-Label Classification in Python Scikit-multilearn is a BSD-licensed library for multi-label classification that is built on top of the well-known scikit-learn ecosystem. The Dogs versus Cats Redux: Kernels Edition playground competition revived one of our favorite "for fun" image classification challenges from 2013, Dogs versus Cats. Logistic Regression using Python Video The first part of this tutorial post goes over a toy dataset (digits dataset) to show quickly illustrate scikit-learn’s 4 step modeling pattern and show the behavior of the logistic. There are different types of tasks categorised in machine learning, one of which is a classification task. TensorFlow : Open-source library developed by Google to train deep neural networks. Scikit learn offers a very wide rang of clasifiers with different characteristics. SVC that implements support vector classification. November 2015. 1 classification. Whether you've loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them. Tutorial exercises. Take pride in good code and documentation. These steps: instantiation, fitting/training, and predicting are the basic workflow for classifiers in Scikit-Learn. Multi-Class Text Classification with Scikit-Learn. scikit-learn v0. March 2015. But there you have it. Packages and applications¶. PDF | scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. Scikit-Learn It does a lot of the dirty work involved with machine learning, and allows you to quickly build models, make predicitons, and manage your data. Machine learning originated from pattern recognition and computational learning theory in AI. The class is intrumented to be use with the scikit-learn cross validation. Multi-Label Classification in Python Scikit-multilearn is a BSD-licensed library for multi-label classification that is built on top of the well-known scikit-learn ecosystem. Clustering. We want to predict for a given image, which digit it depicts. 10 comments. General examples. sparse matrices. Dataset class ¶. Such weights aren't different from the coefficients of a linear regression, and the network learns their value by repeated passes (iterations or epochs) over the examples of the dataset. What You Will Learn. Should know how to extract certain. deviation of Gaussian of the blob found. Image classification with Python. Multilabel Classification with scikit-learn and Probabilities instead of Simple Labels. ndarray and convertible to that by numpy. Using custom components in a scikit-learn model. It is designed to better use NumPy and SciPy libraries of Python. Pipelining: chaining a PCA and a logistic regression The PCA does an unsupervised dimensionality reduction, while the logistic regression does the p. prediction, classification and clustering of data. The decision boundaries, are shown with all the points in the training-set. Scikit-learn: Builds on top of Numpy and Scipy to provide machine learning algorithms like regression, classification, clustering etc. KNN used in the variety of applications such as finance, healthcare, political science, handwriting detection, image recognition and video recognition. Traditional Machine Learning. The rule of thumb is as follows: use multiprocessing when extracting features for all possible ROI in an image; use threading when extracting the feature at specific location for a limited number of ROIs. Ranging from handwritten digit recognition to document classification, examples are solved step by step using Scikit-learn and Python. In Chapter 6 of the book Natural Language Processing with Python there is a nice example where is showed how to train and test a Naive Bayes classifier that can identify the dialogue act types of instant messages. Content-based image classification in Python. Scikit-learn is an open-source software project that aims at making machine learning accessible to all, whether it be in academia or in industry. The image are gray scale, very good contrast, no specific issue in single character contour detection and little problems due to lighting or shadows. com: Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (9781491962299) by Aurélien Géron and a great selection of similar New, Used and Collectible Books available now at great prices. Python is very popular in a lot of machine learning applications, simply due to its simplicity and the wide community of its Scikit-Learn machine learning library. scikit-learn also uses CBLAS, the C interface to the Basic Linear Algebra Subprograms library. After training a model with logistic regression, it can be used to predict an image label (labels 0–9) given an image. datasets import fetch_mldata from sklearn. In this video we will learn how to recognize handwritten digits in python using machine learning library called scikit learn. The Scikit-learn Endpoint you create with deploy runs a SageMaker Scikit-learn model server. Most of the classification examples start with iris dataset, so let’s pick another dataset within scikit-learn for this workflow. scikit-image is a collection of algorithms for image processing. In the model the building part, you can use the cancer dataset, which is a very famous multi-class classification problem. 2 scikit-learn developers Jun 28, 2017 CONTENTS 1 2 3 4 5. scikit-learn documentation: Cross-validation, Model evaluation; scikit-learn issue on GitHub: MSE is negative when returned by cross_val_score; Section 5.