Google Professional Machine Learning Engineer Question 211
Select 3Google Cloud PlatformYou are tasked with building a machine learning model to predict customer churn for a subscription-based service. The dataset contains customer demographic details, subscription history, and usage patterns. Before training the model, what are important considerations you need to evaluate to ensure the model is robust and reliable?
- A
Assess the class imbalance in the dataset and determine if resampling techniques are necessary.
- B
Ensure the dataset includes only numerical features, as categorical features are not suitable for machine learning models.
- C
Analyze the feature distributions to identify potential outliers and decide if they should be removed or treated.
- D
Split the dataset into training, validation, and test sets to evaluate model performance and prevent data leakage.
- E
Select a pre-trained model without analyzing the dataset, as it will save significant time and resources.
Show answer and explanation
Correct answers: A, C, D
Explanation
When building a machine learning model, it is important to thoroughly analyze the dataset to address issues such as class imbalance, outliers, and potential data leakage. Proper preprocessing steps, such as splitting the dataset and considering feature engineering, ensure the model is robust and reliable. Neglecting these considerations can lead to biased, overfitted, or underperforming models.
- A. Correct.
Assessing class imbalance is critical, especially for a classification task like churn prediction. Imbalanced classes can lead to biased models, and techniques like over-sampling, under-sampling, or using appropriate evaluation metrics (e.g., F1-score) might be necessary.
- B. Incorrect.
This is incorrect because categorical features can be used in machine learning models after proper preprocessing, such as one-hot encoding or label encoding. Excluding them unnecessarily may result in loss of valuable information.
- C. Correct.
Analyzing feature distributions and addressing outliers is important to ensure data quality, as outliers can negatively impact model performance by skewing patterns or introducing noise.
- D. Correct.
Splitting the dataset properly is essential to evaluate the model's ability to generalize and to prevent data leakage, which can lead to overly optimistic performance results during training.
- E. Incorrect.
Selecting a pre-trained model without analyzing the dataset is not a good practice. The choice of the model should depend on the dataset characteristics and problem requirements.