Training data, in the context of machine learning, refers to a labeled dataset used to train a machine learning model. It is the initial set of data that the model uses to learn patterns, relationships, and features from input examples and their corresponding output labels.
In supervised learning, which is the most common type of machine learning, the training data consists of pairs of input samples and their corresponding target labels. The model learns from these examples to make predictions or classify new, unseen data accurately.
Key aspects…
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Labeled Examples: Each sample in the training data has an associated ground truth label, which serves as the correct answer that the model aims to learn.
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Quantity: The amount of training data can significantly impact the performance of a machine learning model. Larger and diverse datasets generally help the model generalize better to new, unseen data.
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Data Quality: High-quality and accurate labels are essential for effective model training. Inconsistent or incorrect labels can lead to poor model performance.
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Data Preprocessing: Preprocessing is often necessary to ensure that it is in a suitable format. This may include resizing images, normalizing numerical features, or handling missing values.
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Data Split: The training data is typically split into subsets for training and validation purposes. The validation set is used to monitor the model's performance and do hyperparameter tuning.