In time series forecasting, there is a general rule of thumb that a decent model should always have more observations than parameters in the time series.
For most time series applications, this means that the submitted data should have as many observations as the period of the maximum expected seasonality. In time series forecasting there is a general rule of thumb that a decent model should always have more observations than parameters in the time series. For time series problems, you should always have more observations than parameters (we elaborate more on this type of machine learning problem below).A more general rule of thumb is that the number of observations should be proportional to 1/d^p where p = # of features and d = the maximum spacing between consecutive or neighboring data points after each feature is scaled to the range 0-1. For many regression problems, it’s suggested that you have 10x as many observations as you do features.Sentiment analysis or document classification problems can require thousands of examples due to the sheer number of words and phrases, i.e.A typical image classification problem could require tens of thousands of images or more in order to create a classifier.
It depends on the type of machine learning problem you want to solve: So how much data is necessary to train a decent model that will generalize well, i.e.
Is this model likely to make accurate predictions? Probably not. How much data do you need to train a model? Arguably, only a single data point. While there is no “one-size-fits-all” approach, there are some general best practices to follow and questions to ask about your data beforehand. We’ve gotten some questions recently about how much data is needed to train a good model.
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