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In machine learning, models can experience a phenomenon known as “drift,” where the underlying data distribution evolves, causing the model’s predictions to become less accurate. So, drift detection is the process of identifying the changes in the distribution of data over time.
1. Data drift
This phenomenon occurs when the distribution of the data itself changes. This can happen for a variety of reasons, such as changes in the population that the data is drawn from, or changes in the way that the data is collected.
Example: A machine learning model is trained to predict the likelihood of a customer purchasing a product based on their age and income. The model is trained on a dataset of historical data. Over time, the distribution of customer ages and incomes may change. This is known as data drift.
2. Concept drift
This type of drift happens when the relationship between the features and the target variable changes. This can happen for a variety of reasons, such as changes in the underlying business process, or changes in the way that the target variable is defined.
Example: A machine learning model is trained to predict whether a customer will click on an ad based on the content of the ad. The model is trained on a dataset of historical data. Over time, the types of ads that customers click on may change. This is known as concept drift.
Here, I provide a Python tutorial using TensorFlow to implement a drift detection mechanism:
- Install TensorFlow
pip install tensorflow
2. Import Required Libraries
import tensorflow as tf
import numpy as np
3. Data Collection: Preparing/collecting historical data that represents the model’s performance in the absence…