When it comes to data, there’s a lot that can go wrong. Whether it’s incorrectly labeling training data, or forgetting to include some important variables in your code, mistakes can happen a lot. But what if you could predict those mistakes before they happened? That’s the power of machine learning, and it’s exactly what we’re…
Lets us try to implement logistic regression from scratch in python. Recommended to be read after the Neural Networks release. Importing necessary libraries The dataset we will be using is Pima-Indians-diabetes-database Whose objective is to predict whether or not a patient has diabetes diagnostically. y = data.Outcome.values x_data = data.drop([“Outcome”],axis=1) After this, the data…
Logistic regression is one of the most popular algorithms for classification problems. It is called regression even though it is not a regression algorithm because the underlying technology is similar to Linear Regression. The term “logistic” comes from the statistical model used (logit model). As seen in earlier releases, classification algorithms are used to classify…
Classification algorithms depend on the dataset being used, and data scientists have curated various algorithms that can be used in certain situations. Most popular types of Classification Algorithms: Linear Classifiers Logistic regression Naive Bayes classifier Support vector machines Kernel estimation k-nearest neighbor Decision trees Random forests Let’s discuss this in more detail and understand when…
Under supervised learning, there is a type called classification. These algorithms recognize the category a new observation belongs to based on the training dataset. In supervised learning, there are independent variables and a dependent variable Here, the dependent variable is the category, and each category’s features are independent variables. These categories are distinct and pre-defined…
