Machine Learning with scikit-learn

Machine Learning with scikit-learn

Machine Learning with Scikit-Learn: An In-Depth Guide

Machine Learning (ML) is a powerful tool for extracting patterns from data and making accurate predictions about the future. ML algorithms can be used to identify trends, forecast outcomes, and make decisions without the need for manual intervention. Scikit-learn is an open source ML library developed in Python that has become the de-facto standard for ML in Python. In this guide, we’ll cover the basics of ML and how to use Scikit-learn to get the most out of your data.

What is Machine Learning?

Machine Learning is a subfield of artificial intelligence that focuses on giving machines the ability to “learn” from data. ML algorithms are designed to identify patterns in data and then use those patterns to make predictions about future data points. ML algorithms can be used to identify trends in financial markets, forecast customer behaviour, and identify potential fraud. ML algorithms can also be used to make decisions without the need for manual intervention; for example, a ML algorithm can be used to detect potential fraud transactions and automatically block them without the need for human review.

Types of Machine Learning

There are two main types of ML algorithms: supervised and unsupervised. Supervised learning algorithms are trained using labeled data - that is, data that has been tagged with the correct outcome. For example, a supervised learning algorithm could be trained using labeled images of cats and dogs. The algorithm would then be able to predict whether a new image is of a cat or a dog. Unsupervised learning algorithms, on the other hand, are trained using unlabeled data. An unsupervised learning algorithm might be used to identify clusters in a dataset, for example.

Implementing Machine Learning with Scikit-Learn

Scikit-learn is a powerful open source ML library written in Python. It provides tools for data preprocessing, feature extraction, model selection, and model evaluation. Scikit-learn also provides implementations of many popular ML algorithms, including: linear regression, logistic regression, support vector machines, decision trees, random forests, naïve Bayes, and many more. In this guide, we’ll focus on using Scikit-learn for supervised and unsupervised learning.

Setting up the Environment

In order to use Scikit-learn, you’ll need to install Python and a few other dependencies. The easiest way to get started is to install the Anaconda distribution of Python, which includes Scikit-learn and all the other dependencies you’ll need. Once you’ve installed Anaconda, you can test your installation by running the following command in a terminal window:


$ python
Python 3.7.3 (default, Mar 27 2019, 22:11:17) 
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import sklearn
>>> print(sklearn.__version__)
0.22

The above code should print out the current version of Scikit-learn. If it does, then your installation was successful!

Features of Scikit-Learn

Scikit-learn has a number of features that make it easy to implement ML algorithms. First, Scikit-learn provides a consistent interface for all of its ML algorithms. This means that once you’ve learned how to use one algorithm, you’ll be able to use any other algorithm in Scikit-learn in the same way. Second, Scikit-learn provides a number of helpful functions that make it easy to preprocess data, extract features, and evaluate models. Finally, Scikit-learn has an active user community that provides helpful advice and support.

Preprocessing Data with Scikit-Learn

Before you can use a ML algorithm, you’ll need to preprocess your data. Preprocessing includes tasks like scaling data, filling in missing values, and converting data from one format to another. Scikit-learn provides a number of helpful preprocessing functions, including:

  • StandardScaler: scales data so that it has zero mean and unit variance
  • MinMaxScaler: scales data so that all features are between 0 and 1
  • Imputer: fills in missing values with the mean, median, or mode
  • LabelEncoder: converts categorical data (strings) into numerical data

You can find more information in the Scikit-learn documentation.

Implementing Supervised Learning Algorithms with Scikit-Learn

Supervised learning algorithms are used to predict outcomes from data. Scikit-learn provides implementations of the following supervised learning algorithms:

  • Linear Regression: used for predicting continuous outcomes
  • Logistic Regression: used for predicting binary outcomes
  • Support Vector Machines (SVMs): used for classification tasks
  • Decision Trees: used for classification and regression tasks
  • Random Forests: used for classification and regression tasks
  • Naïve Bayes: used for classification tasks

For each algorithm, you’ll need to define the model (e.g. linear regression), set the hyperparameters (e.g. regularization coefficient), and then fit the model to the data. Once the model is fit, you can use it to make predictions.

Implementing Unsupervised Learning Algorithms with Scikit-Learn

Unsupervised learning algorithms are used to identify clusters in data. Scikit-learn provides implementations of the following unsupervised learning algorithms:

  • K-means clustering: used for clustering data
  • Hierarchical clustering: used for clustering data
  • Latent Dirichlet Allocation (LDA): used for topic modeling
  • t-SNE: used for dimensionality reduction

For each algorithm, you’ll need to define the model (e.g. k-means clustering), set the hyperparameters (e.g. number of clusters), and then fit the model to the data. Once the model is fit, you can use it to identify clusters in the data.

Model Evaluation and Optimization with Scikit-Learn

Once you’ve trained a model, you’ll need to evaluate its performance. Scikit-learn provides a number of metrics for evaluating ML models, including accuracy, precision, recall, and the F1 score. You can also use cross-validation to get an estimate of a model’s performance on unseen data. Finally, you can use Scikit-learn’s grid search function to optimize a model’s hyperparameters.

Conclusion

In this guide, we’ve covered the basics of ML and how to use Scikit-learn to get the most out of your data. We’ve covered the features of Scikit-learn, how to preprocess data, how to implement supervised and unsupervised learning algorithms, and how to evaluate and optimize models. Machine learning is a powerful tool for extracting patterns from data and making accurate predictions about the future. And with Scikit-learn, it’s never been easier to get started with ML in Python.

Further Learning

If you’d like to learn more about ML and Scikit-learn, we recommend the following resources:

  • Data Science from Scratch by Joel Grus
  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
  • Python Machine Learning by Sebastian Raschka
  • The Scikit-learn Documentation