Databricks Machine Learning Associate Question 495
Select 3You are working on a machine learning project in Databricks using a large dataset. The dataset contains missing values, duplicate records, and inconsistent data types for some columns. Which of the following steps should you take during data preprocessing to ensure the data is ready for training a machine learning model?
- A
Handle missing values by imputing or removing them.
- B
Convert all categorical columns into numerical representations using techniques like one-hot encoding or label encoding.
- C
Drop rows with duplicate records without analyzing their impact on the dataset.
- D
Standardize or normalize numerical features to ensure they are on the same scale.
- E
Remove columns with any missing values to avoid complications during model training.
Show answer and explanation
Correct answers: A, B, D
Explanation
Data preprocessing is a critical step in machine learning workflows. It involves cleaning the data to ensure quality and preparing it in a format suitable for model training. Handling missing values, encoding categorical variables, and normalizing numerical features are essential steps to improve model performance and reliability. However, blindly removing duplicates or columns with missing values can lead to loss of valuable information and should be avoided unless justified.
- A. Correct.
Handling missing values is critical in data preprocessing to ensure the dataset does not introduce bias or errors during model training. Techniques like imputation or removal are standard practices.
- B. Correct.
Converting categorical columns into numerical representations is necessary because machine learning models generally do not work with non-numerical data.
- C. Incorrect.
Dropping duplicate records without assessing their impact may lead to loss of important data or distributional imbalance. It's better to analyze and decide based on the context.
- D. Correct.
Standardizing or normalizing numerical features ensures that features are on the same scale, which is particularly important for models sensitive to feature magnitudes, such as gradient descent-based models.
- E. Incorrect.
Removing columns with any missing values is often unnecessary and could lead to loss of critical information. Instead, handling missing values appropriately is a better approach.