Databricks Machine Learning Professional Question 58
Select 4You are working on a machine learning pipeline in Databricks and need to preprocess a dataset for a classification model. The dataset contains missing values, categorical features, and a column with highly skewed numerical data. Which of the following preprocessing steps should you implement to ensure the data is properly prepared for the model?
- A
Impute missing values using mean or median for numerical columns and the most frequent value for categorical columns.
- B
Encode categorical features using one-hot encoding or label encoding.
- C
Apply logarithmic transformation to the skewed numerical column to reduce skewness.
- D
Normalize the numerical features using min-max scaling after handling missing values and skewness.
- E
Drop all rows with missing values to avoid potential data quality issues.
Show answer and explanation
Correct answers: A, B, C, D
Explanation
Preprocessing is a critical step in machine learning pipelines to ensure data quality and improve model performance. This includes handling missing values, encoding categorical data, addressing skewness in numerical features, and normalizing the data. These steps collectively prepare the dataset for effective training and prevent issues such as data loss or model bias.
- A. Correct.
Correct: Imputation is a standard preprocessing step to handle missing values. Using mean or median for numerical columns and the most frequent value for categorical columns ensures the dataset remains usable without significantly altering the data distribution.
- B. Correct.
Correct: Encoding categorical features is necessary to convert them into numerical representations that machine learning models can understand. One-hot encoding or label encoding are common techniques for this purpose.
- C. Correct.
Correct: Highly skewed numerical data can negatively affect model performance. Applying a logarithmic transformation reduces skewness and makes the data more suitable for modeling.
- D. Correct.
Correct: Normalizing numerical features ensures that all features are on the same scale, which can improve the convergence of gradient-based optimization algorithms used in many machine learning models.
- E. Incorrect.
Incorrect: Dropping all rows with missing values can lead to significant data loss, especially if many rows contain missing values. Imputation is a better approach in most scenarios.