COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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That is at least 95% accurate on the training data set
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That takes less than 30 minutes to train and validate
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That makes you feel like you are a true machine learning expert
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That is at the sweet spot between a simple working model and a very complex one
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Detailed explanation-1: -Model tuning is the experimental process of finding the optimal values of hyperparameters to maximize model performance. Hyperparameters are the set of variables whose values cannot be estimated by the model from the training data. These values control the training process.
Detailed explanation-2: -Tuning is the process of maximizing a model’s performance without overfitting or creating too high of a variance. In machine learning, this is accomplished by selecting appropriate “hyperparameters.” Hyperparameters can be thought of as the “dials” or “knobs” of a machine learning model.
Detailed explanation-3: -Grid search is a hyperparameter tuning method in which we create a grid of possible discrete hyperparameter values, then fit the model with every possible combination. Grid search is an exhaustive algorithm that can find the best combination of hyperparameters.
Detailed explanation-4: -In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned.