What are some ways to use feature engineering to handle redundant features in your machine learning dataset?

Correlation analysis will assess the linear or non-linear connection between different characteristics. Depending on the nature and distribution of your characteristics, you can use different correlation coefficients such as Pearson, Spearman, or Kendall. and visualize using a heatmap or a scatter plot to find pairings of attributes with high correlation. Then you may delete characteristics that have a strong correlation with other features since they anyway provide little value to your model. However, depending on problem's context certain characteristics may have a strong correlation but distinct interpretations or consequences, and hence those needn't be deleted.

Excellent analysis and suggestions. One more thing I want to add here is to beware of inconsistent data samples as those may lead to the highest correlation with target labels, while ending up with model overfitting due to bias problems. Such scenarios can be easily identified by reducing the feature matrix to a single column and you will learn the structure correlating with target labels.

PCA is a useful feature engineering technique. It transforms the original features into a new set of features which are linear combinations of the original features, that capture maximum variance in the data. These new sets of features are called principal components. Pros: - PCA is useful in reducing the dimensionality of data - PCA doesn't consider the target variable to compute the principal components, making it suitable for larger datasets and also computationally efficient - PCA can filter out noisy and irrelevant information from the data making it beneficial for tasks like visualization and anomaly detection Cons: - interpretability of the features is lost - sensitive to the scaling of data

Principal component analysis (PCA), is a dimensionality reduction method that converts your original features into a new set of features known as principle components. Principal components are linear combinations of your initial characteristics that capture as much volatility in your data as possible. PCA helps minimize the number of features required while retaining the majority of the information in your data. However, PCA has numerous limitations, including losing feature interpretability, assuming linear correlations among features, and being susceptible to outliers and scaling.

Feature selection in machine learning tackles redundant features through three main methods: filter, wrapper, and embedded. Filter methods rank features using statistical tests, focusing on individual feature relevance. Wrapper methods, on the other hand, use a search algorithm to assess feature subsets based on model performance. Embedded methods are integrated into model training, using techniques like regularization and tree-based algorithms for selection. Each method uniquely reduces dataset complexity, improving model accuracy and interpretability.

feature Selection is?a process of automatically or manually selecting the subset of most appropriate and relevant features to be used in model building

Best ways to start - Principal component analysis - Correlation analysis between features and target variable - Perturbation analysis for features - Shap values based analysis

Also, in finalizing your pipelines, it is highly recommended to use data cleaning and imputation as a transformer block. The use of simple imputer in a pipeline may also assist during the model deployment stage, as each real world data sample will be cleaned and imputed automatically.

Excellent analysis. What I want to add here is the estimator based feature selection as well. During the model development stage, we may use a variety of estimators, which are usually optimized in accordance with certain feature columns. I also recommend using tree based feature selectors, which can also be visualised as highly, mediocre, and poorly correlated features in order to assist tree based models in structure learning. Hence, a practitioner may select a certain set of feature columns in accordance with local constraints (time, cost, computation, and energy).