How to create and optimise a baseline DecisionTree Model for Multiclass Classification in Python Decision Trees are a popular method of statistical analysis that can be used to predict a categorical variable based on a set of input variables. They are very helpful for both classification and regression problems. In this article, we will go …

How to create and optimise a baseline DecisionTree Model for Binary Classification in Python Decision Trees are a popular method of statistical analysis that can be used to predict a categorical variable based on a set of input variables. They are very helpful for both classification and regression problems. In this article, we will go …

How to optimise hyper-parameters of a DecisionTree Model using GridSearchCV in Python When building a machine learning model, it’s important to optimize the parameters of the model for the best performance. One way to do this is by tuning the hyper-parameters of a DecisionTree model using GridSearchCV. A Decision Tree model is a simple and …

How to optimise hyper-parameters of a Logistic Regression Model using GridSearchCV in Python When building a machine learning model, it’s important to optimize the parameters of the model for the best performance. One way to do this is by tuning the hyper-parameters of a logistic regression model using GridSearchCV. Logistic regression is a statistical method …

How to select model using Grid Search in Python When building a machine learning model, it’s important to pick the right model that works best for the dataset. One way to do this is by using grid search to compare different models. Grid search is a method that allows you to evaluate multiple models with …

How to plot ROC Curve in Python When building a machine learning model for a binary classification problem, it’s important to evaluate its performance using various metrics. One way to do this is by plotting an ROC (Receiver Operating Characteristic) curve. An ROC curve is a graph that shows the relationship between the model’s true …

How to generate Classification Report and Confusion Matrix in Python When building a machine learning model, it’s important to evaluate its performance using various metrics. One way to do this is by creating a classification report and a confusion matrix. A classification report includes metrics such as precision, recall, f1-score, and support for each class …

How to check model’s Average Precision score using Cross Validation in Python When building a machine learning model, it’s important to evaluate its performance using various metrics. One of them is the Average Precision (AP) score, which is a measure of a model’s ability to retrieve relevant instances among a large number of irrelevant instances. …

How to check model’s AUC score using Cross Validation in Python When building a machine learning model, it’s important to evaluate its performance using various metrics. One of them is the AUC (Area Under the Curve) score, which measures the model’s ability to predict binary outcomes. Cross-validation is a method that allows to test the …

How to check model’s recall score using Cross Validation in Python When building a machine learning model, it’s important to evaluate its performance using various metrics. One of them is the recall score, which measures the proportion of true positive predictions out of all the actual positive observations in the dataset. Cross-validation is a method …