Training data

Here are some key points about training data in machine learning:

• Training data is the dataset that is used to fit the parameters of a machine learning model. It is used during the training process.
• It consists of examples that contain features (input variables) and labels (expected output variable). Features can be categorical, ordinal or continuous.
• The model learns patterns and relationships between features and labels from the training data in order to make predictions on new unlabeled data.
• Training data must be representative of the real-world data that the model will encounter. This allows for better generalization.
• The amount of training data required depends on the complexity of the problem and model. More complex problems typically need more training data.
• Models are prone to overfitting when there is too little training data compared to the number of parameters in the model.
• Training data can be divided into training and validation sets to monitor model performance during training and tune hyperparameters.
• For supervised learning tasks, labels must be accurately assigned to examples in the training data. Noise, errors, or bias in labels degrades model performance.
• Training data may need preprocessing like formatting, cleaning, feature selection, normalization/scaling depending on the requirements of the model.
• High-quality, representative training data is crucial for building effective machine learning models that can generalize well.