Applied Data Science Coding with Python: How to get Classification Confusion Matrix A confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known. The matrix is used to visualize the model’s predictions and compare them …
Applied Data Science Coding with Python: How to get Classification AUC ROC AUC-ROC (Area Under the Receiver Operating Characteristic curve) is a commonly used metric to evaluate the performance of a binary classification model. It is a graphical representation of the model’s ability to distinguish between the two classes, and it can be used to …
Assignment in Python In Python, assignment is the process of giving a value to a variable. A variable is a named location in the computer’s memory that can be used to store data. By using the assignment operator (=), a value can be stored in a variable, which can then be used in various operations …
How to check installed version of SciPy SciPy is a powerful library for scientific computing in Python. It’s common to check the version of SciPy that’s currently installed to ensure compatibility with other packages or to make sure you have the latest features. One way to check the version of SciPy that’s installed is by …
How to check installed version of scikit-learn Scikit-learn is a powerful library for machine learning in Python. It’s common to check the version of scikit-learn that’s currently installed to ensure compatibility with other packages or to make sure you have the latest features. One way to check the version of scikit-learn that’s installed is by …
How to find optimal parameters for CatBoost using GridSearchCV for Classification in Python To find the optimal parameters for CatBoost using GridSearchCV for Classification in Python, you can follow these steps: Define the CatBoostClassifier model and specify the range of parameter values you want to test. These can include parameters such as depth, learning rate, …
How to find optimal parameters for CatBoost using GridSearchCV for Regression in Python To find the optimal parameters for CatBoost using GridSearchCV for Regression in Python, you can follow these steps: Define the CatBoost model and specify the range of parameter values you want to test. These can include parameters such as depth, learning rate, …
How to find optimal parameters using RandomSearchCV in Regression in Python In machine learning, finding the best set of parameters for a model is an important step to achieve the best performance. One technique to find the optimal parameters is RandomizedSearchCV. RandomizedSearchCV is a method for parameter tuning in which random combinations of the parameters …
How to find optimal parameters using GridSearchCV in Regression in Python GridSearchCV is a method to find the best set of parameters for a machine learning model. It works by defining a range of parameters that you want to test and then evaluating the performance of the model for each combination of parameters. The goal …
How to find optimal parameters using RandomSearchCV in Python In machine learning, finding optimal parameters for a model is an important step to achieve good performance. One way to find optimal parameters is by using the RandomizedSearchCV function provided by the scikit-learn library in Python. RandomizedSearchCV is similar to GridSearchCV, but instead of trying every …