Classification in R – logistic regression for binary class classification in R

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Classification in R – logistic regression for binary class classification in R

Classification is a type of supervised machine learning that is used to predict the class or category of a new observation based on the values of its predictors. One popular method of classification is using logistic regression for binary class classification.

Logistic regression is a statistical method that is used to model the relationship between a binary dependent variable and one or more independent variables. It is called logistic because it uses the logistic function to model the probability of the binary outcome. The logistic function produces a probability value between 0 and 1, which can then be used to predict the probability of the binary outcome (e.g. 0 or 1, yes or no, success or failure, etc.). Logistic regression can be used for binary classification problems, where the goal is to predict one of two possible outcomes.

In R, there are several packages available for building logistic regression models, such as the ‘stats’ and ‘glm’ packages. These packages provide functions for creating and training logistic regression models, as well as functions for evaluating the performance of the model.

The process of building a logistic regression model in R typically involves the following steps:

Prepare the data: The first step is to prepare the data for the model. This may involve cleaning the data, splitting it into training and testing sets, and creating dummy variables for categorical predictors.

Define the model: The next step is to define the structure of the model, including the dependent and independent variables, and the type of logistic regression (e.g. simple or multiple).

Train the model: The model is trained using the prepared data. The model will find the best coefficients for the independent variables that maximize the likelihood of the observed binary outcomes.

Evaluate the model: The model’s performance is evaluated using various metrics such as accuracy, precision, recall, and F1 score.

Make predictions: Once the model is trained and evaluated, it can be used to make predictions on new data by inputting the values of the independent variables and using the logistic function to predict the probability of the binary outcome.

Logistic regression is a simple and interpretable model, it’s also good at dealing with high-dimensional data. It can handle categorical variables as well. However, logistic regression assumes that the relationship between the predictor variables and the outcome is linear, which may not be true in some cases. It also assumes that the data is independent and identically distributed, which may not always be the case.

 

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: Classification in R – logistic regression for binary class classification in R.

Classification in R – logistic regression for binary class classification in R

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