LabelEncoder is a utility class provided by the scikit-learn library in Python, specifically in the sklearn.preprocessing module. It is commonly used for encoding categorical labels into numerical labels. Here’s what LabelEncoder does: Here’s an example of how to use LabelEncoder: Keep in mind that LabelEncoder is suitable for encoding target labels (dependent variables) in supervised…
Bagging (Bootstrap Aggregating) and Boosting are both ensemble learning techniques that aim to improve the predictive performance of machine learning models by combining multiple base learners. However, they differ in their approach to training and how they leverage the base learners’ predictions to improve model performance. Bagging focuses on reducing variance, whereas Boosting focuses on…
XGBoost stands for eXtreme Gradient Boosting, and it’s an optimized and highly scalable implementation of the Gradient Boosting framework. Developed by Tianqi Chen and now maintained by the Distributed (Deep) Machine Learning Community, XGBoost has gained widespread popularity in machine learning competitions and real-world applications due to its efficiency, flexibility, and outstanding performance. XGBoost Parameters…
Gradient Boosting is another ensemble learning technique used for classification and regression tasks and has its own specific way of building the ensemble of weak learners. Here’s a brief overview of Gradient Boosting: Gradient Boosting typically produces more accurate models compared to AdaBoost but can be more computationally expensive and prone to overfitting, especially with…
AdaBoost (Adaptive Boosting) is a popular ensemble learning algorithm used for classification and regression tasks. It works by combining multiple weak learners (typically decision trees, often referred to as “stumps”) to create a strong learner. Here’s how it generally works: AdaBoost is effective because it focuses on improving the classification of difficult examples by giving…
The BaggingClassifier is an ensemble meta-estimator in machine learning, belonging to the bagging family of methods. Bagging stands for Bootstrap Aggregating. The main idea behind bagging is to reduce variance by averaging the predictions of multiple base estimators trained on different subsets of the training data. Here’s how the BaggingClassifier works: The BaggingClassifier in scikit-learn…
ogistic Regression is a statistical method used for binary classification tasks, where the outcome variable is categorical and has two classes. Despite its name, it is used for classification rather than regression. The logistic regression algorithm models the probability that a given input belongs to a particular class. The logistic regression model applies the logistic…
In scikit-learn’s GridSearchCV (Grid Search Cross Validation), the parameter cv stands for “cross-validation.” It determines the cross-validation splitting strategy to be used when evaluating the performance of a machine learning model. When cv is set to an integer (e.g., cv=5), it represents the number of folds in a (Stratified) K-Fold cross-validation. For example, cv=5 means…
Now put the printed best_model here below and run:
Grid search is a tuning technique that attempts to compute the optimum values of hyperparameters. It is an exhaustive search that is performed on the specific parameter values of a model. The parameters of the estimator/model used to apply these methods are optimized by cross-validated grid-search over a parameter grid.
