Target Drift (also known as concept drift) refers to changes in the distribution of the target variable (output) over time, which can affect the performance of machine learning models. When target drift occurs, the relationship between the input features and the target variable that the model was originally trained on may no longer hold. This can lead to degraded model accuracy and predictions that are no longer reliable.

Types of Drift

1. Target Drift:This occurs when the distribution of the target variable itself changes over time. For example, if you're predicting customer churn, the percentage of customers who churn might change over time due to changes in market conditions or company policies.
2. Feature Drift: Feature drift occurs when the distribution of input features changes over time, but the relationship between the features and the target variable remains unchanged.
3. Concept Drift: Concept drift is a more general term that includes both target drift and changes in the underlying relationships between input features and the target variable. Concept drift can involve changes in the entire data distribution or specific relationships between features and the target.

Examples of Target Drift

1. Financial Markets: In financial services, target drift may occur when market conditions change due to external factors like economic downturns or policy changes, leading to shifts in asset prices or customer investment behavior.
2. Healthcare: In healthcare, treatment outcomes or disease prevalence might change over time due to advances in medical treatments or changes in population health, causing target drift in predictive models for medical diagnoses or treatment outcomes.

Impact of Target Drift on Machine Learning Models

• Decreased Accuracy: As the target distribution shifts, models trained on outdated data may make inaccurate predictions because they no longer reflect the current reality.

• Increased Error Rates: Predictions can become less reliable over time as target drift increases, leading to higher error rates and degraded model performance.

• Need for Frequent Retraining: To combat target drift, models often need to be retrained more frequently on updated data to ensure that they can capture the current relationships between features and targets.

Causes of Target Drift

1. Seasonality: Seasonal changes can cause shifts in the target distribution. For instance, retail sales may increase during holiday periods, leading to a temporary change in the target distribution.
2. Policy Changes: Regulatory or business policy changes can alter the outcomes (targets) of certain processes. For example, a change in loan approval policies may affect loan default rates, which would lead to target drift in a model predicting defaults.
3. Market or Environment Shifts: Changes in market conditions, competition, or consumer trends can alter the target distribution. For example, shifts in consumer demand due to economic changes might affect product sales forecasts.
4. Data Collection Process: Changes in how data is collected or measured can also lead to target drift. If a company changes how it defines a certain outcome, such as redefining what constitutes "customer churn," it may alter the target distribution over time.