Classification models also tend to be more flexible than regression models, as they can handle various types of data. Add your perspective Help others by sharing more (125 characters min.) Cancel ...

Model Selection: Compared the performance of Logistic Regression and SVM. Chose Logistic Regression for its balance of performance, speed, and ease of understanding, despite SVM's higher F1 score.

Regression and classification models used for predictive analytics can suffer from overfitting or underfitting, which can lead to a lack of generalization to unseen data.

So, after finishing your predictive, classification, or regression model—here is a list of evaluation metrics that can help you test the accuracy and concreteness of the model. Confusion Matrix.

Logistic regression is a binary classification algorithm that uses input features to model the probability of lung cancer presence given these input features using for example a sigmoid function.

This project uses logistic regression to classify Iris species. Through exploratory data analysis, model training, and evaluation with Scikit-learn in Python, it achieves high accuracy in identifying ...

Two CNN models were trained: one for regression, predicting continuous aesthetic scores, and another for classification, categorizing images into discrete rating intervals. Performance evaluation ...

This study evaluates the predictive performance of three machine learning models—Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM)—for classifying CHD. The models were ...