![]() If you have hourly data and you expect your data exhibits weekly seasonality, you should have more than 7*24 = 168 observations to train a model. For most time series applications, this means that the submitted data should have as many observations as the period of the maximum expected seasonality.įor example, if you have daily sales data and you expect that it exhibits annual seasonality, you should have more than 365 data points to train a successful model. 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. DataRobot Success Stories See how organizations like yours have realized more value from their AI initiatives.Deployment Infrastructure Choose how you want to deploy DataRobot, from managed SaaS, to private or public cloud.Platform Integrations Unify your data warehouses, ML APIs, workflow tooling, BI tools and business apps.Monitor and Measure ROI Monitor, measure and diagnose model accuracy, ROI, and bias in real-time from any hosting environment.Integrate Models Deploy and integrate any model, anywhere with multiple deployment options.Validate and Govern Models Create a centralized system of record for all models, test, approve, and automate compliance documentation.Make Business Decisions Evaluate model performance, identify key drivers, and create customizable apps to drive decisions.Build Models Train hundreds of modeling strategies in parallel using structured and unstructured data.Prepare Modeling Data Connect data, assess data quality, engineer new features, and integrate with feature stores. ![]() Discover the DataRobot AI Platform The only fully open, end-to-end AI lifecycle platform with deep ecosystem integrations and applied AI expertise. ![]()
0 Comments
Leave a Reply. |