Note<\/strong>: Logistic Regression is a Classification algorithm and hence discussed under Classification<\/p>\n You already learnt from Class 6 (Introduction to Classification) that given values of x, we need to determine the class, y.\u00a0 In other words, we aim to find the function that relates x and y. In the case of linear regression, this function is of the form:<\/p>\n y = f(x) = b0 + b1x<\/p>\n However, in the case of logistic regression, the output must be 0 or 1. So to achieve this, we need two things:<\/p>\n To solve the first issue, we would model the outputs as probabilities. For example, we have a dataset of bank customers with credit card debt and we want to predict which customers will default. The input variable would be the balance on the customers account. The function will be something like this<\/p>\n Pr(default = Yes | balance)<\/p>\n This is shortened as p(balance) and will always give a value between 0 and 1 (since probability values are in range of 0 and 1)<\/p>\n The solve the second issue we have to choose a threshold. For example, we may predict default = Yes for customers whose p(balance) > 0.5.<\/p>\n Before we go into the practical, I would just want you to know about the logistic function which is give below.<\/p>\n <\/p>\n Ok, let’s now go into the fun part!<\/p>\n <\/p>\n All the necessary modules we need are given below. I’m sure by now, you know what each of them are used for:<\/p>\n <\/p>\n We generate a dataset here using the make_classification function from sklearn.datasets.<\/p>\n You can actually change the number of samples to something more.<\/p>\n <\/p>\n Let’s now just do a scatter plot of this data. So we see why linear regression would not work quite well1. The Logistic Regression Model<\/strong><\/h5>\n
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2. Import the Necessary Modules<\/strong><\/h5>\n
from<\/span> sklearn.datasets<\/span> import<\/span> make_classification\r\nfrom<\/span> matplotlib<\/span> import<\/span> pyplot as<\/span> plt\r\nfrom<\/span> sklearn.linear_model<\/span> import<\/span> LogisticRegression\r\nfrom<\/span> sklearn.model_selection<\/span> import<\/span> train_test_split\r\nfrom<\/span> sklearn.metrics<\/span> import<\/span> confusion_matrix\r\nimport<\/span> pandas<\/span> as<\/span> pd<\/span>\r\n<\/pre>\n3. Generate the Dataset<\/strong><\/h5>\n
# Generate and dataset for Logistic Regression<\/span>\r\nx, y =<\/span> make_classification(\r\n n_samples=<\/span>100<\/span>,\r\n n_features=<\/span>1<\/span>,\r\n n_classes=<\/span>2<\/span>,\r\n n_clusters_per_class=<\/span>1<\/span>,\r\n flip_y=<\/span>0.03<\/span>,\r\n n_informative=<\/span>1<\/span>,\r\n n_redundant=<\/span>0<\/span>,\r\n n_repeated=<\/span>0<\/span>\r\n)\r\n<\/pre>\n4. Visualize the Data Using ScatterPlot<\/strong><\/h5>\n
\n<\/p>\n# Create a scatter plot<\/span>\r\nplt.<\/span>scatter(x, y, c=<\/span>y, cmap=<\/span>'rainbow'<\/span>)\r\nplt.<\/span>title('Scatter Plot of Logistic Regression'<\/span>)\r\nplt.<\/span>grid( linestyle=<\/span>'--'<\/span>)\r\nplt.<\/span>show()\r\n<\/pre>\n
![\"Logistic](\"https:\/\/www.kindsonthegenius.com\/data-science\/wp-content\/uploads\/sites\/12\/2021\/09\/Screenshot-2021-09-30-at-21.41.30-300x212.png\")
<\/a>![\"Sigmoid](\"https:\/\/www.kindsonthegenius.com\/data-science\/wp-content\/uploads\/sites\/12\/2021\/09\/Screenshot-2021-09-30-at-22.02.41-300x200.png\")
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