Tag: improving ML model performance

Data Cleaning: This involves handling missing values, removing duplicates, and correcting inconsistencies in the dataset. Techniques include imputation (filling missing values with mean, median, or mode) and removing rows or columns with excessive missing data.

Data Transformation: Transforming data into a suitable format for analysis is essential. This includes normalization (scaling features to a specific range) and standardization (scaling features to have a mean of 0 and a standard deviation of 1).

Feature Encoding: Categorical variables need to be converted into numerical format for machine learning algorithms. Common encoding techniques include one-hot encoding (creating binary columns for each category) and label encoding (assigning a unique integer to each category).

Feature Selection: Identifying and selecting the most relevant features can enhance model performance and reduce overfitting. Techniques include correlation analysis, recursive feature elimination, and using feature importance from models like random forests.

Data Splitting: Dividing the dataset into training, validation, and test sets is essential for evaluating model performance. A common split ratio is 70% for training, 15% for validation, and 15% for testing.

Data Cleaning: This involves handling missing values, removing duplicates, and correcting inconsistencies in the dataset. Techniques include imputation (filling missing values with mean, median, or mode) and removing rows or columns with excessive missing data.

Data Transformation: Transforming data into a suitable format for analysis is essential. This includes normalization (scaling features to a specific range) and standardization (scaling features to have a mean of 0 and a standard deviation of 1).

Feature Encoding: Categorical variables need to be converted into numerical format for machine learning algorithms. Common encoding techniques include one-hot encoding (creating binary columns for each category) and label encoding (assigning a unique integer to each category).

Feature Selection: Identifying and selecting the most relevant features can enhance model performance and reduce overfitting. Techniques include correlation analysis, recursive feature elimination, and using feature importance from models like random forests.

Data Splitting: Dividing the dataset into training, validation, and test sets is essential for evaluating model performance. A common split ratio is 70% for training, 15% for validation, and 15% for testing.