Task 0: Naïve Logistic Regression Make a logistic regression and report the accuracy. Task 1: Train Data Transformation Perform the pre-processing to transform the original data into a new feature space by doing feature engineering so the features are linear in the new space. Confirm four assumptions required for a linear classifier. Task 2: Linear Parametric Classification Implement logistic regression model using Scikit-learn. Using the GridSearchCV, optimize the model. 1. Make a logistic regression model. Report the weights and the accuracy of the model. 2. Using the GridSearchCV at various 100 a values from 10-5 to 10, build a logistic regression model. Visualize how the model accuracy behaviors. Then report the best model. If the accuracy is 100%, then the model is overfitted. In this case, the model should be regularized. 3. Using the best model, classify the test data set. Task 3: Transformation using Kernel Method Kernelize the original to a Kernel space using five different valid Kernel functions. Then repeat Task 2. Task 4: Non-parametric KNN Classification 1. Classify the original data with K values from 1 to 200. Then report the accuracy with visualization. 2. Repeat step 1 with the final train data sets from Tasks 1 and 3. Report: Write a report summarizing the work. In the report, all steps must be explicitly explained with visualizations.

Solved: Task 0: Naïve Logistic Regression Make a logistic regression and repo

Home
questions and answers
task 0 naive logistic regression make a logistic regression and report

Question

Task 0: Naïve Logistic Regression Make a logistic regression and report the accuracy. Task 1: Train Data Transformation Perform the pre-processing to transform the original data into a new feature space by doing feature engineering so the features are linear in the new space. Confirm four assumptions required for a li