Data Preprocessing and Model Evaluation in Machine Learning

What method was used to handle outliers in the dataset?

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

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

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

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

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

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

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

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

Why was the Random Forest algorithm chosen for model building?

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

Why was it concluded that the XGBoost model was overfitting?

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

What does a higher R2 score indicate about model performance?

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

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

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

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