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…
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…
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…
In the context of the train_test_split function in machine learning, the stratify parameter is used to ensure that the splitting process preserves the proportion of classes in the target variable. When you set stratify=y, where y is your target variable, the data is split in a way that maintains the distribution of classes in both…
t-SNE, which stands for t-distributed Stochastic Neighbor Embedding, is a popular dimensionality reduction technique (of type Feature Extraction) used in machine learning and data visualization. It is particularly useful for visualizing high-dimensional data in a lower-dimensional space, typically two or three dimensions, while preserving the local structure of the data as much as possible. The…
