Description: Data adjustment is the process of modifying and preparing datasets with the aim of improving the performance of a machine learning model. This process involves various techniques that may include data cleaning, normalization, feature transformation, and variable selection. Data cleaning refers to the removal of outliers, missing data, or inconsistencies that can affect the quality of the model. Normalization and standardization are techniques that adjust the scale of the data so that all features contribute equally to the model. Feature transformation may include creating new variables from existing ones, which can help capture more complex patterns. Finally, variable selection involves identifying and retaining only those features that are most relevant to the model, which can reduce complexity and improve interpretability. In the context of machine learning, data adjustment becomes even more crucial, as these tools seek to automatically optimize model performance, and a well-adjusted dataset is essential for achieving accurate and reliable results.
