TensorFlow: Handling Missing Values & Outliers

In TensorFlow, dealing with missing and outlier values is typically done during the data preprocessing stage. Here are some common methods for handling them:

1. Remove missing values and outliers: You can either eliminate samples that contain missing values or outliers, or eliminate feature columns that contain missing values or outliers.
2. Replace missing values: missing values can be replaced with the mean, median, mode, or a specific value.
3. Fill in missing values using interpolation methods: interpolation methods such as linear interpolation, polynomial interpolation, spline interpolation, etc. can be used to estimate missing values.
4. Identifying and handling outliers using anomaly detection algorithms: Anomaly detection algorithms such as Isolation Forest and LOF can be used to recognize and manage outliers.

In TensorFlow, you can use methods in the tf.data.Dataset class for data preprocessing. For example, using the parameter skipna=True can skip missing values, and using the batch method can split the data into batches. Additionally, you can use TensorFlow’s data transformation and handling functions to deal with missing and outlier values in the data.