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Do you scale both training and test data?
The test set must use identical scaling to the training set. And the point is given that: Do not scale the training and test sets using different scalars: this could lead to random skew in the data. Could someone explain what that means?
How are models chosen in training and validation?
As we currently look at 20 models that are only aware of the training data, we validate them all on the validation data set. Through this step, we are provided with accuracy scores for each model based on their performance on the validation data. We simply choose the one with the highest score.
What happens if you don’t have quality training data?
Without a foundation of high-quality training data, even the most performant algorithms can be rendered useless. Indeed, robust machine learning models can be crippled when they are trained on inadequate, inaccurate, or irrelevant data in the early stages.
How to normalize training and test data at the same time?
The right way to do this is to use only the training set to calculate the mean and variance, normalize the training set, and then at test time, use that same (training) mean and variance to normalize the test set.
How big does training data need to be?
Regarding training data size, they report that performance increases with growing data size; however, it plateaus after 50 million images.
What do you mean by training data in machine learning?
The following are several frequently asked questions when it comes to training data in machine learning: What is training data? Neural networks and other artificial intelligence programs require an initial set of data, called a training dataset, to act as a baseline for further application and utilization.
Is it OK to use statistics on only the training set?
The main reason to use statistics computed on only the training set is to avoid leaking information from the test set. If this is not a concern, then it is perfectly OK to use statistics from the entire data set. See here for further discussion.
Where does your training and testing data come from?
But, clearly this web dataset comes from a different distribution, with high resolution and clear images such as the following: Images of dogs (left and right) and a fox (center). How would you build the train/dev/test sets?
How are data sets divided into training and test sets?
The previous module introduced the idea of dividing your data set into two subsets: training set —a subset to train a model. test set —a subset to test the trained model.
Why is feature scaling only to training set?
As with all the transformations, it is important to fit the scalers to the training data only, not to the full dataset (including the test set). Only then can you use them to transform the training set and the test set (and new data)