How logistic regression problem can be solved by Machine Learning Algorithms? How logistic regression problem solving technique is different from linear regression problem solving using machine learning?
Ashish
Dear Aparna,
Your paper is very useful.
This is a supervised learning classification algorithm (most popular machine learning algorithms is used for solving the classification problems) and used to predict the probability of a target variable( suppose variable is binary). Basically, it is a predictive analysis algorithm and based on the concept of probability.One can import the model class, train the model, make predictions, and print accuracy and confusion matrix using algorithm programming. Accuracy vs. threshold optimization could be drawn easily using algorithm programming output. Confusion matrix is much important part by which machine can learn on their interface using solution of classification problem in logistics.
Ashish
Dear Ashish Thakur ,
Thank you for your knowledgeable question. To answer your question directly, Logistic regression is mainly for Binary classification, which is similar to the Support vector machine (SVM). Linear regression, on the other hand, is good for predicting continuous value(E.g., price of a house) as against discrete types(Good or Bad) for Logistic regression.
Dear Stanley,
Correct. It would have been better if you enlighten on which machine algorithm SVM works and how it is useful in solving logistic regression problem.what is the platform they use and how it solves....continuous and discrete both may come in regression so how it curtails the point to solve the graph using algorithm?
Ashish
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