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· 03 Jul 2024

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What is Semi-supervised Machine Learning?

Semi-supervised machine learning is a type of machine learning where algorithms are trained on a combination of labeled and unlabeled data. In traditional supervised learning, algorithms only learn from labeled data, where each input is paired with the correct output. However, labeled data can be expensive and time-consuming to obtain, whereas unlabeled data is often more readily available in larger quantities.

In semi-supervised learning, the algorithm leverages both the small amount of labeled data and the larger amount of unlabeled data to improve learning accuracy. The idea is that the unlabeled data provides additional information about the underlying structure of the data, which can help the algorithm generalize better and make more accurate predictions.

Here’s an overview of semi-supervised learning:

Labeled Data:
Labeled data consists of input samples paired with corresponding output labels or target values. These labeled data points are used to train supervised learning models, where the algorithm learns to make predictions based on input-output pairs.

Unlabeled Data:
Unlabeled data consists of input samples without corresponding output labels. Unlike supervised learning, where every data point is labeled, unlabeled data is abundant and often easier to acquire in real-world scenarios.

Model Training:
In semi-supervised learning, the algorithm utilizes both labeled and unlabeled data during the training process. The labeled data is used in a similar manner to supervised learning, where the algorithm learns from labeled examples to make predictions or extract patterns.
Additionally, the unlabeled data is leveraged to improve the model’s performance by incorporating information from the unlabeled samples into the learning process. This can help the model learn a better representation of the underlying data distribution and generalize more effectively to unseen data.

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