Error analysis

You can use error analysis to find common characteristics between data points with incorrectly predicted outputs. For example, the majority of images that are misclassified in image classification models might have darker backgrounds, or a disease diagnostic model might have lower performance for men compared to women. Although manually investigating the data points with incorrect predictions could be insightful, this process could cost you a lot of time. Instead, you can try to reduce the cost programmatically.

Here, we want to practice with a simple case of error analysis in which the number of misclassified data points from each class is counted for a random forest model that’s been trained and validated using a 5-fold CV. For error analysis, only predictions for validation subsets are used.

First, we must import the necessary Python libraries and load the wine dataset:

from sklearn.datasets import load_winefrom sklearn.ensemble import RandomForestClassifier...