How to create and optimise a baseline Linear Regression Model in Python Linear Regression is a popular method of statistical analysis that can be used to predict a continuous variable based on a set of input variables. In this article, we will go over the basics of how to create and optimize a linear regression …
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 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 tune hyper-parameters 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 using GridSearchCV. GridSearchCV is a method that allows you to search for the best combination of hyper-parameters, by …
How to plot Validation Curve in Python When building a machine learning model, it’s important to evaluate its performance and optimize its parameters. One way to do this is by plotting a validation curve. A validation curve is a graph that shows the relationship between a model’s performance and a specific hyperparameter value. It helps …
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 plot Learning Curve in Python When building a machine learning model, it’s important to evaluate its performance and make sure it’s not overfitting or underfitting. One way to do this is by plotting a learning curve. A learning curve is a graph that shows the relationship between the model’s performance and the amount …
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 …