Regularization is a technique that shrinks the coefficient estimates towards zero. This technique adds a penalty to more complex models and discourages learning of more complex models to reduce the chance of overfitting.
Bias is the difference between actual value and the predicted value that a model predicts. In machine learning, data is fed to the machine learning model, the model finds the patterns from data and learns from data.
Imbalance dataset is such a type of dataset that has an unequal distribution of data among the classes of classification of datasets. Most machine learning algorithms work well with balanced datasets.
K-Nearest Neighbor(KNN) algorithm is a supervised machine learning algorithm that is used for classification and regression analysis.
k-means clustering is an easy yet powerful algorithm in machine learning that is used for clustering the data in different clusters. It is centroid based clustering in machine learning.
Hyperparameter tuning is an essential term in the field of machine learning. Hyperparameter is a set of parameters that control the learning process of the algorithm.