The Dangers of Data Dredging in Machine Learning

Data dredging is a common problem that can creep into machine learning workflows, often without us even realizing it. In this article, we'll explore what data dredging is and why it's a practice to be avoided.

Loading the Boston Housing Dataset

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To demonstrate the dangers of data dredging, let's start with an example using the Boston Housing dataset from scikit-learn. We'll load the data and split it into training and testing sets.

```python

from sklearn.datasets import load_boston

from sklearn.model_selection import train_test_split

boston = load_boston()

X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.2, random_state=0)

```

Data Dredging: A Quick Explanation

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In a nutshell, data dredging is the practice of using your testing or validation set to inform decisions about your model-building or hyper-parameter tuning. This might seem harmless at first, but it can lead to biased models and poor generalization performance.

Here's an example of what data dredging might look like in practice:

```python

from sklearn.ensemble import GradientBoostingRegressor

gbm = GradientBoostingRegressor()

params = {'n_estimators': randint(10, 100), 'learning_rate': '0.1, 0.5, 0.9'}

kfold = KFold(n_splits=5, shuffle=True, random_state=42)

rs = RandomizedSearchCV(gbm, param_grid=params, cv=kfold)

rs.fit(X_train, y_train)

print('Best parameters:', rs.best_params_)

```

In this example, we're using the testing set to select the best hyper-parameters for our Gradient Boosting Regressor. This is an example of data dredging because we're allowing our test data to inform our model-building decisions.

Why Data Dredging is a Problem

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Data dredging can lead to several issues in machine learning, including:

* Overfitting: By repeatedly exposing your testing set to your model and adjusting hyper-parameters, you're essentially overfitting to the noise in the data.

* Biased models: Data dredging can result in biased models that don't generalize well to new, unseen data.

* Poor performance: Models that are trained using data dredged techniques tend to perform poorly on independent test sets.

How to Avoid Data Dredging

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To avoid data dredging and ensure the integrity of your machine learning models, follow these best practices:

* Use separate holdout sets: Split your data into training, testing, and validation sets. Use the testing set only for evaluating model performance and the validation set for tuning hyper-parameters.

* Don't overfit to your test data: Be careful not to overfit your model to your testing data by repeatedly adjusting hyper-parameters or making changes to your model based on its performance on the test set.

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Data dredging is a common problem in machine learning that can lead to biased models, poor performance, and overfitting. By understanding what data dredging is and how to avoid it, you can ensure that your machine learning models are robust and reliable. Remember to use separate holdout sets, don't overfit to your test data, and keep your model-building decisions independent of your testing set.