Wine Quality Model Evaluation

This problem simulates the evaluation of three machine learning pipelines applied to the Wine Quality dataset. In this simulation, three different feature selection techniques are used: Recursive Feature Elimination (RFE), Lasso-based selection, and Principal Component Analysis (PCA). Each technique is used to train a Logistic Regression model whose performance is measured using four metrics: accuracy, precision, recall, and F1‐score (all values lie in the range \( [0, 1] \)).

Your task is to write a program that reads input from stdin (even if the input is irrelevant) and then prints the evaluation metrics for each method to stdout in the following format:

RFE: accuracy precision recall f1
Lasso: accuracy precision recall f1
PCA: accuracy precision recall f1

For this problem, you can assume the following constant metrics (which simulate the evaluation outputs):

• RFE: 0.55 0.52 0.53 0.51
• Lasso: 0.54 0.51 0.53 0.50
• PCA: 0.56 0.53 0.54 0.52

Make sure your output matches exactly (including the order and spacing) as shown above.

inputFormat

The input is taken from stdin and may contain arbitrary text, but it should be ignored. It is provided only to comply with competitive programming standards.

You do not need to parse or use this input in your solution.

outputFormat

Your program must print exactly three lines to stdout corresponding to the evaluation results of RFE, Lasso, and PCA respectively. Each line must follow the format:

Method: acc prec rec f1

with the following constant values:

• RFE: 0.55 0.52 0.53 0.51
• Lasso: 0.54 0.51 0.53 0.50
• PCA: 0.56 0.53 0.54 0.52

## sample

RFE: 0.55 0.52 0.53 0.51
Lasso: 0.54 0.51 0.53 0.50
PCA: 0.56 0.53 0.54 0.52

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