{"id":14630,"date":"2020-08-28T05:41:11","date_gmt":"2020-08-28T12:41:11","guid":{"rendered":"https:\/\/www.springboard.com\/?p=14630"},"modified":"2022-10-13T09:37:51","modified_gmt":"2022-10-13T16:37:51","slug":"scikit-learn","status":"publish","type":"post","link":"https:\/\/www.springboard.com\/blog\/data-science\/scikit-learn\/","title":{"rendered":"Scikit-Learn: A Complete Guide With a Logistic Regression Example"},"content":{"rendered":"\n

Scikit-Learn is a machine learning library that includes many supervised and unsupervised learning algorithms. To date, Scikit-Learn is the first stop for most data scientists and machine learning engineers to build their first machine learning model or set a benchmark for further experiments. This is handy because you don\u2019t always need complex and computationally expensive deep learning algorithms to model your data.<\/p>\n\n\n\n

In this article, we will focus on logistic regression and its implementation on the MNIST dataset using Scikit-Learn, a free software machine learning library for Python.<\/p>\n\n\n\n

What is Scikit-Learn logistic regression used for?<\/h2>\n\n\n\n

There are two primary problems in supervised machine learning: regression and classification (just to let you know almost 70% problems in data science<\/a> are classification problems). Logistic regression (the term logistic regression is a “fake friend” because it does not refer to regression) is a classification algorithm used for classification problems, such as determining whether a tumor is malignant or benign and assessing automotive types. It is essential for an ML engineers or Data Scientists<\/a> to have a clear understanding on logistic regression.<\/p>\n\n\n\n

In simple terms, logistic regression is the process of finding the best possible plane (decision boundary, Figure 1) that separates classes under consideration. It also assumes that these classes are linearly separable.<\/p>\n\n\n\n

\"screen<\/a><\/figure>\n\n\n\n

Figure 1: Sample decision plane in 2D (Source: jeremyjordan.me)<\/em><\/p>\n\n\n\n

Since linear regression is a fundamental building block of machine learning, we\u2019ll use this concept as a jumping-off point to explain the mathematics of logistic regression.<\/p>\n\n\n\n

The main difference between linear regression and logistic regression is the output function. Linear regression uses a linear function that outputs continuous values in any range, whereas logistic regression uses a sigmoid function that limits outputs in the range of zero to one.<\/p>\n\n\n

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