Feature Engineering §

Group: 4 #group-4

Relations §

• Dimensionality Reduction: Dimensionality reduction techniques are used to reduce the number of features while preserving important information.
• Machine Learning: Feature engineering is a crucial step in preparing data for machine learning models.
• Machine Learning: Feature Engineering is the process of selecting and transforming relevant features from raw data to improve the performance of Machine Learning models.
• Data Mining: Feature engineering is a key step in the data mining process to extract meaningful patterns from data.
• Feature Scaling: Feature scaling is used to normalize or standardize features to prevent bias in the model.
• Encoding Categorical Variables: Categorical variables need to be encoded into numerical form for use in machine learning models.
• Predictive Modeling: Feature engineering is essential for building accurate and reliable predictive models.
• Model Performance: Effective feature engineering can significantly improve the performance of machine learning models.
• Handling Missing Data: Missing data needs to be handled appropriately through techniques like imputation or removal.
• Feature Selection: Feature selection is a key step in feature engineering to identify the most relevant features.
• Exploratory Data Analysis: Exploratory data analysis helps guide feature engineering by understanding the data characteristics.
• Machine Learning: Feature Engineering is the process of selecting, transforming, and creating new features from raw data to improve the performance of Machine Learning models.
• Underfitting: Proper feature engineering can help mitigate underfitting by providing the model with more informative and relevant features.
• Data Preprocessing: Feature engineering involves data preprocessing techniques to prepare the data for modeling.
• Feature Extraction: Feature extraction techniques are used to derive new features from existing ones.
• Feature Construction: Feature construction involves creating new features from existing ones through mathematical or domain-specific transformations.
• Data Transformation: Data transformation techniques are applied to convert data into a suitable format for modeling.