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What do you mean by noise in the dataset?

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In the context of machine learning, “noise” in a dataset refers to the presence of random or irrelevant variation in the data that does not reflect the underlying relationship between the input features and the target variable. This variation can come from various sources, such as measurement errors, outliers, missing values, or sampling bias.

Noise can be problematic for machine learning algorithms because it can obscure the true signal in the data and lead to overfitting, where the model captures the noise instead of the underlying pattern. This can result in poor performance on new, unseen data.

Dealing with noisy data is a common challenge in machine learning, and several techniques have been developed to address it, such as regularization, feature selection, data cleaning, and robust modeling approaches like Gradient Boosting Machines (GBM) that can adapt to noise.

In general, it is important to identify and handle noise in the data appropriately to ensure that the machine learning model learns the true underlying pattern and generalizes well to new data.

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