**Regularization** is commonly employed when dealing with anomalies to mitigate their undue impact on predictive models. While regularization **may not directly identify outliers**, its primary role is to **reduce the influence of outliers on the model's results**. 

Instead of explicitly detecting outliers, it focuses on **making the model more robust and less sensitive to extreme data points**.
## Regularisation types


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 <th>L1 Regularization (Lasso)</th>
 <th>L2 Regularization (Ridge)</th>
 <th>Dropout Regularization</th>
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 <th>Purpose</th>
 <td>L1 regularization adds a penalty term to the loss function based on the absolute values of the model's coefficients. It encourages some coefficients to become exactly zero, effectively performing feature selection.</td>
 <td>L2 regularization adds a penalty term to the loss function based on the squares of the model's coefficients. It tends to keep all feature coefficients small but doesn't force them to be exactly zero.</td>
 <td>Dropout is a regularization technique commonly used in neural networks. During training, dropout randomly deactivates a fraction of neurons in each layer. This helps prevent overfitting by reducing the reliance on specific neurons or features.</td>
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 <th>Impact on Anomalies</th>
 <td>L1 regularization can help make the model more robust to anomalies by reducing the impact of less important features. Anomalies may have less influence on the final model because some feature coefficients are pushed to zero. However, it may not completely eliminate the effect of outliers.</td>
 <td>Ridge regularization can make the model less sensitive to anomalies by keeping feature coefficients small. It helps prevent extreme values in coefficients caused by outliers, leading to a more stable model.</td>
 <td>Dropout can improve the model's robustness to anomalies by preventing it from becoming overly dependent on specific data points, including outliers. It encourages the network to learn more generalized representations, which can help in handling unexpected or noisy data.</td>
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How does L2 regularization (Ridge) impact a model's sensitivity to anomalies or outliers in the data?

Anomaly detection is integral to any data scientist's work: high-quality, cleaned, and well-prepared data is the key to success for almost any machine learning problem.

Let's delve into the concept of data anomalies, their classification, and their impact on decision-making. Throughout this exploration, we will gain a comprehensive understanding of these topics, accompanied by real-life examples to illustrate their significance.

Statistical methods play a crucial role in various domains, including finance, cybersecurity, manufacturing, and healthcare, where detecting anomalies can prevent fraud, ensure data quality, and improve overall system reliability. These methods are particularly valuable when dealing with large datasets and complex systems, as they provide a systematic and quantitative approach to anomaly detection.

Now, let's explore how machine learning methods can effectively address anomalies. We will examine various techniques, including clustering, regularization, dropout, and more, that are employed for this purpose.

Data Anomaly Detection

Regularisation

Regularisation types