Month: April 2020

How to check installed version of NumPy | Jupyter Notebook | Python Data Science for beginners

How to check installed version of NumPy   NumPy is a powerful library for scientific computing in Python. It provides an array object, a powerful N-dimensional array that is the foundation for many other libraries such as SciPy, Matplotlib and Pandas. NumPy is widely used for mathematical and scientific computations in Python, and it is …

How to check installed version of Matplotlib | Jupyter Notebook | Python Data Science for beginners

How to check installed version of Matplotlib   Matplotlib is a powerful library for creating visualizations in Python. It is widely used for creating plots, charts, and other types of graphics. It is an open-source library and can be easily installed using the pip package manager. To check the version of Matplotlib that is currently …

Boosting ensembles with depth parameter tuning using yeast dataset in Python

Boosting ensembles with depth parameter tuning using yeast dataset in Python   Boosting ensemble classifiers are a powerful method for improving the performance of a model in classification tasks. These classifiers are a combination of multiple weak models that work together to make a more accurate prediction. One important aspect of boosting ensemble classifiers is …

How to compare boosting ensemble Classifiers in Multiclass Classification

How to compare boosting ensemble Classifiers in Multiclass Classification     When it comes to classification tasks, there are many different machine learning models and techniques that can be used. Boosting ensemble classifiers are one popular method that can be used to improve the performance of a model. Boosting ensemble classifiers are a combination of …

How to apply LightGBM Classifier to yeast dataset

How to apply LightGBM Classifier to yeast dataset     LightGBM is a powerful machine learning library that can be used to improve the performance of decision tree models. It is particularly useful for large datasets and datasets with a lot of features. In this essay, we will be discussing how to use the LightGBM …

How to apply CatBoost Classifier to yeast dataset

How to apply CatBoost Classifier to yeast dataset     CatBoost is a powerful machine learning library that can be used to improve the performance of decision tree models. It is especially useful for datasets with categorical features and is known for its ability to handle missing data and categorical features automatically. In this essay, …

How to apply XGBoost Classifier to yeast dataset

How to apply XGBoost Classifier to yeast dataset XGBoost is a powerful machine learning library that can be used to improve the performance of decision tree models. It is especially useful for large datasets and for datasets with a lot of features. In this essay, we will be discussing how to use the XGBoost library …

How to apply Gradient Boosting Classifier to yeast dataset

How to apply Gradient Boosting Classifier to yeast dataset     Gradient Boosting Classifier is a powerful machine learning technique that can improve the performance of decision tree models by training multiple trees on different subsets of the data and then combining the predictions of all the trees to make a final prediction. In this …

How to compare Bagging ensembles in Python using yeast dataset

How to compare Bagging ensembles in Python using yeast dataset     Bagging ensembles are a powerful machine learning technique that can improve the performance of decision tree models by training multiple trees on different subsets of the data and then combining the predictions of all the trees to make a final prediction. The technique …

How to apply sklearn Extra Tree Classifier to yeast dataset

How to apply sklearn Extra Tree Classifier to yeast dataset   Extra Trees (Extremely Randomized Trees) is an ensemble technique that is used to improve the performance of decision tree models. Like random forests, it works by training multiple decision trees on different subsets of the data and then combining the predictions of all the …