n Pandas library in Python pd.qcut is a function for performing quantile-based discretization of continuous variables. Quantile-based discretization involves dividing a continuous variable into discrete intervals or bins based on the distribution of its values. This process ensures that each bin contains approximately the same number of observations, making it useful for creating categories or…
RandomizedSearchCV is a method provided by scikit-learn for hyperparameter tuning and model selection through cross-validation. It’s similar to GridSearchCV, but instead of exhaustively searching through all possible combinations of hyperparameters, it randomly samples a fixed number of hyperparameter settings from specified distributions. Here’s a basic overview of how RandomizedSearchCV works: Here’s a basic example of…
get_params() is a method provided by scikit-learn estimators (such as classifiers, regressors, transformers, etc.) that returns a dictionary of the estimator’s parameters. These parameters are the hyperparameters that define the behavior of the estimator and can be tuned during the model selection or hyperparameter optimization process. Here’s a simple example of how you might use…
SimpleImputer is a class in scikit-learn, a popular machine learning library in Python, used for handling missing values in datasets. It provides a simple strategy for imputing missing values, such as filling missing entries with the mean, median, most frequent value, or a constant. Here’s a basic example of how you might use SimpleImputer: This…
Tomek Link Undersampling is a technique used to address class imbalance in machine learning datasets. It involves identifying Tomek links, which are pairs of instances from different classes that are nearest neighbors of each other, and removing instances from the majority class that form these links. The main idea behind Tomek Link Undersampling is to…
SMOTE (Synthetic Minority Over-sampling Technique) is an upsampling technique used in machine learning to address the class imbalance problem, which occurs when the number of instances of one class (minority class) is significantly lower than the number of instances of the other class (majority class) in a dataset. This class imbalance can lead to biased…
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…
