Category: Dimensionality Reduction In ML

Principal Component Analysis (PCA) is a popular unsupervised dimensionality reduction technique in machine learning used to transform high-dimensional data into a lower-dimensional representation. PCA is used to identify patterns and structure in data by discovering the underlying relationships between variables. It is commonly used in applications such as image processing, data compression, and data visualization.…

Missing Values Ratio is a feature selection technique used in machine learning to identify and remove features from the dataset that have a high percentage of missing values. This technique is used to improve the performance of the model by reducing the number of features used for training the model and to avoid the problem…

Low Variance Filter is a feature selection technique used in machine learning to identify and remove low variance features from the dataset. This technique is used to improve the performance of the model by reducing the number of features used for training the model and to remove the features that have little or no discriminatory…

High Correlation Filter is a feature selection technique used in machine learning to identify and remove highly correlated features from the dataset. This technique is used to improve the performance of the model by reducing the number of features used for training the model and to avoid the problem of multicollinearity, which occurs when two…

Forward Feature Construction is a feature selection method in machine learning where we start with an empty set of features and iteratively add the best performing feature at each step until the desired number of features is reached. The goal of feature selection is to identify the most important features that are relevant for predicting…

Backward Elimination is a feature selection technique used in machine learning to select the most significant features for a predictive model. In this technique, we start by considering all the features initially, and then we iteratively remove the least significant features until we get the best subset of features that gives the best performance. Implementation…

Feature extraction is often used in image processing, speech recognition, natural language processing, and other applications where the raw data is high-dimensional and difficult to work with. Example Here is an example of how to perform feature extraction using Principal Component Analysis (PCA) on the Iris Dataset using Python − In this code, we first…

Feature selection is an important step in machine learning that involves selecting a subset of the available features to improve the performance of the model. The following are some commonly used feature selection techniques − Filter Methods This method involves evaluating the relevance of each feature by calculating a statistical measure (e.g., correlation, mutual information,…

Dimensionality reduction in machine learning is the process of reducing the number of features or variables in a dataset while retaining as much of the original information as possible. In other words, it is a way of simplifying the data by reducing its complexity. The need for dimensionality reduction arises when a dataset has a…