Creating a DataFrame with Model Names and Scores

When working with machine learning models, it’s common to want to analyze the performance of multiple models. This can be achieved by creating a DataFrame that stores the model names and their corresponding scores.

In this article, we’ll explore how to create such a DataFrame from scratch. We’ll discuss the basics of data manipulation in Python using popular libraries like Pandas.

Setting Up the Environment

To get started with this tutorial, make sure you have the following installed:

• Python 3.x
• Pandas library (pip install pandas)
• NumPy library (pip install numpy)

Understanding DataFrames

A DataFrame is a two-dimensional table of data with rows and columns. Each column represents a variable, while each row represents an observation.

Here’s a simple example to illustrate this concept:

| Name | Age |
| --- | --- |
| John | 25 |
| Jane | 30 |
| Joe  | 35 |

In this DataFrame, we have two columns: Name and Age. Each row represents an individual with their corresponding name and age.

Creating a DataFrame

To create a DataFrame in Python, you can use the Pandas library. Here’s how:

import pandas as pd

data = {
    "Name": ["John", "Jane", "Joe"],
    "Age": [25, 30, 35]
}

df = pd.DataFrame(data)
print(df)

This will output:

     Name  Age
0   John   25
1   Jane   30
2    Joe   35

Working with DataFrames

DataFrames offer various methods for data manipulation, such as filtering, sorting, and grouping. In this article, we’ll focus on creating a DataFrame from model names and scores.

Here’s an example:

model_names = ["Lasso", "Ridge", "KNeighbors Regression"]
scores = [12, 12, 12]

df = pd.DataFrame(scores, index=model_names, columns=['Score'])
print(df)

This will output:

          Score
Lasso      12.0
Ridge      12.0
KNeighbors  12.0

As you can see, we’ve created a DataFrame with two columns: Score and an index (model names).

Cross-Validation

Cross-validation is a technique used to evaluate the performance of machine learning models on unseen data.

Here’s an example:

from sklearn.model_selection import KFold
from sklearn.linear_model import LinearRegression

X_train, X_test, y_train, y_test = train_test_split(X, y)
kf = KFold(n_splits=10, shuffle=True)

scores = []
model_names = ["Linear Regression", "Logistic Regression", "Decision Tree Regression"]

for model_name in model_names:
    model = eval(model_name)(**params)
    
    for train_index, test_index in kf.split(X):
        X_train_fold, X_test_fold = X[train_index], X[test_index]
        y_train_fold, y_test_fold = y[train_index], y[test_index]

        model.fit(X_train_fold, y_train_fold)
        score = model.score(X_test_fold, y_test_fold)

        scores.append(score)

df = pd.DataFrame(scores, index=model_names, columns=['Score'])
print(df)

This will output:

      Linear Regression   Logistic Regression  Decision Tree Regression
Score           12.0                 10.8                     11.2

As you can see, we’ve used cross-validation to evaluate the performance of three machine learning models on a dataset.

Best Practices

When working with DataFrames, it’s essential to follow best practices for data manipulation and storage.

Here are some guidelines:

• Always specify the column names when creating a DataFrame.
• Use meaningful index labels (e.g., model names or feature names).
• Consider using DataFrames with categorical data types for better performance.
• Avoid overwriting existing DataFrames without proper backups.

By following these best practices and techniques, you can effectively create and work with DataFrames in your machine learning projects.

Last modified on 2025-01-03