Python Machine Learning Pipeline

Integrate Pandas,Seaborn,and PyTorch for real-world ML tasks.

1. What is the initial step in a typical Python Machine Learning pipeline?

Data Collection Model Training Hyperparameter Tuning Deployment

2. Which of the following are common data preprocessing techniques in Python ML pipelines? (Select all that apply)

StandardScaler OneHotEncoder RandomForestClassifier Train-Test Split

3. In Python ML pipelines, the 'fit_transform()' method should be applied to both training and test data for preprocessing.

True False

4. Name the Python library primarily used for creating and executing ML pipelines (abbrev.)

5. Which Scikit-learn class chains preprocessing steps and a model into a single pipeline?

Pipeline make_pipeline ColumnTransformer GridSearchCV

6. What is a key benefit of using an ML pipeline in Python?

Increases model complexity Prevents data leakage Eliminates the need for EDA Requires less computational resources

7. Which of the following are components of a typical Python ML pipeline? (Select all that apply)

Exploratory Data Analysis (EDA) Feature Engineering Model Evaluation Cloud Deployment

8. Feature engineering is a critical step in an ML pipeline as it directly impacts model performance.

True False

9. What term describes splitting data into two subsets: one for model training and one for evaluation?

10. Which method is used to train a Scikit-learn Pipeline object?

fit() predict() transform() evaluate()

11. Which metrics are suitable for evaluating regression models in a Python ML pipeline? (Select all that apply)

Mean Squared Error (MSE) Accuracy R-squared F1-Score

12. A Python ML pipeline can include both traditional ML models (e.g., SVM) and deep learning models (e.g., neural networks).

True False

13. What process involves optimizing model parameters (e.g., learning rate, n_estimators) to improve performance?

14. Which step follows model training in a typical ML pipeline?

Model Evaluation Data Collection Feature Engineering Deployment

15. Which tools integrate with Scikit-learn Pipelines to automate hyperparameter tuning? (Select all that apply)

GridSearchCV RandomizedSearchCV StandardScaler LabelEncoder

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