prétraitement

Prétraitement refers to the steps taken to prepare raw data for analysis or modeling. This process is crucial in fields like machine learning, data science, and statistics, as the quality and format of the data significantly impact the outcome of any subsequent operations. Raw data is often messy, incomplete, or inconsistent, making direct analysis difficult or impossible.

Common prétraitement techniques include data cleaning, which involves handling missing values (imputation or removal), correcting errors, and removing duplicates. Data transformation is another key aspect, encompassing normalization or scaling of numerical features to bring them within a specific range, and encoding categorical variables into numerical formats that machine learning algorithms can understand. Feature engineering, the creation of new features from existing ones, is also often considered a part of prétraitement, aiming to improve the predictive power of a model.

Dimensionality reduction, such as Principal Component Analysis (PCA) or feature selection, is employed to reduce the number of input variables, which can help prevent overfitting and speed up computation. The specific prétraitement steps chosen depend heavily on the nature of the data and the goals of the analysis. Effective prétraitement can lead to more accurate, robust, and efficient models.