Data Preprocessing and Model Evaluation in Machine Learning

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Questions and Answers

What method was used to handle outliers in the dataset?

  • StandardScaler method
  • IQR method (correct)
  • Encoding method
  • Feature standardization method

Which algorithm had the smallest MSE value among the three algorithms mentioned?

  • Decision Tree
  • Random Forest (correct)
  • XGBoost
  • Linear Regression

What was the primary evaluation metric used to measure the model's performance?

  • Mean Squared Error (MSE) (correct)
  • Mean Absolute Error (MAE)
  • F1 Score
  • Root Mean Squared Error (RMSE)

How many algorithms were used in the study for building the statistical model?

<p>Five (C)</p> Signup and view all the answers

What determines the worth of the automobile in the study's model?

<p>Multiple attributes including kilometers traveled, fuel type, car model, car brand, and gear type (A)</p> Signup and view all the answers

Which algorithm had the lowest RMSE value among the five algorithms used?

<p>XGBoost (D)</p> Signup and view all the answers

What is the importance of power, transmission, year, engine, and fuel type in influencing the price of a used car?

<p>They are important features (C)</p> Signup and view all the answers

Which algorithm had better performance metrics, Random Forest, or XGBoost?

<p>Random Forest (A)</p> Signup and view all the answers

What was the value of the 'colsample bytree' parameter after hyperparameter tuning for XGBoost?

<p>0.714 (D)</p> Signup and view all the answers

Why was the Random Forest algorithm chosen for model building?

<p>It was not affected by overfitting issues like XGBoost. (B)</p> Signup and view all the answers

Which hyperparameter was tuned for Random Forest that had a value of 33?

<p>max depth (B)</p> Signup and view all the answers

Why was it concluded that the XGBoost model was overfitting?

<p>It showed high R score on the training data but low R score on the testing data. (C)</p> Signup and view all the answers

What is indicated by a high Mean Square Error value in performance evaluation metrics?

<p>The model is overfitting the data. (B)</p> Signup and view all the answers

What does a higher R2 score indicate about model performance?

<p>Better model performance (D)</p> Signup and view all the answers

Which algorithm showed signs of overfitting based on the model evaluation metrics?

<p>XGBoost (C)</p> Signup and view all the answers

Why is it important for performance metrics to be consistent across training and testing data sets?

<p>To verify that the model is generalizing well and not just memorizing the training data. (B)</p> Signup and view all the answers

How can a machine learning algorithm adapt well with new data?

<p>By not being affected by overfitting issues. (B)</p> Signup and view all the answers

What does it mean when the predicted prices closely match the actual prices in model analysis?

<p>The model's predictions are accurate and it's performing well. (C)</p> Signup and view all the answers

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