Techniques for Evaluating Machine Learning Models

A Guide to Unconventional Performance Metrics

Performance evaluation metrics for machine learning algorithms in the context of time series data often focus on aspects such as forecasting accuracy, model interpretability, and computational efficiency.

While there are many standard metrics like mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE), you can explore more exotic or specialized metrics to gain deeper insights into your model’s performance.

Machine Learning and Performance Evaluation

Performance evaluation in time series forecasting is the process of assessing how well a machine learning model predicts future values in a time-ordered sequence. It is crucial for understanding the quality of forecasts, model selection, and refining predictive algorithms.

This article will generate a synthetic time series, fit a linear regression model to understand it, predict the future values, and finally present a few exotic model evaluation metrics.

The generated time series will be a simple and clean sine wave as illustrated in the following graph.

Sine wave

The Python framework of the analysis is as follows:

• Generate a simple sine wave time series.
• Split the time series into a training and test data.
• Fit the model and predict on the test set.
• The following sections will show new evaluation metrics.

Use the code to develop the algorithm:

import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd

def data_preprocessing(data, num_lags, train_test_split):
    # Prepare the data for training
    x = []
    y = []
    for i in range(len(data) - num_lags):
        x.append(data[i:i + num_lags])
        y.append(data[i+ num_lags])
    # Convert the data to numpy arrays
    x = np.array(x)
    y = np.array(y)
    # Split the data into…