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## How much data do you need to train a model?

For 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. 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.

**How much data is needed for training a neural network?**

According to Yaser S. Abu-Mostafa(Professor of Electrical Engineering and Computer Science) to get a proper result you must have data for at-least 10 times the degree of freedom. example for a neural network which has 3 weights you should have 30 data points.

**What percentage of data should be used for training?**

investigated the impact of the proportion of data used in various subsets on ANN model performance for a case study of settlement prediction of shallow foundations and found that there is no clear relationship between the proportion of data for training, testing and validation and model performance, however, they found …

### How much data is needed to forecast?

How Much Data Do You Need to Create an Accurate Forecast? To make a good forecast you need three years of data or more, and to make a great forecast, you need five years.

**What percentage of data should be validated?**

Confirming the lot is 5 to 10 percent of the training set. In most articles its 70% vs 30% for training and testing set respectively.. Normally 70% of the available data is allocated for training. The remaining 30% data are equally partitioned and referred to as validation and test data sets.

**How do you forecast without data?**

7 Steps For Forecasting Without Historical Data

- Start with my current financial position.
- Study the competition’s results.
- Run various conservative and aggressive scenarios using forecasting software.
- Survey customers and prospects.
- Research external factors.
- Account for everything (even in the small stuff).

#### What type of data is useful for forecasting?

In general, forecasting techniques start with data. With some types of forecasting, you’ll use historical data that’s internal to the company, target audience, sales, and growth. Other types of forecasting will be informed by external data, such as competitor analysis and overall industry trajectory.

**How do you know you have enough training data?**

Therefore, as noted in [9], the amount of data needed for learning depends on the complexity of the model. A side effect of this is the well-known voracity of neural networks for training data, given their significant complexity. As Training Data Grows, Will Performance Continue to Improve Accordingly?

**Do you need more data to train a neural network?**

In order to figure out whether or not more data will be helpful, you should compare the performance of your algorithm on the training data (i.e. the data used to train the neural network) to its performance on testing data (i.e. data the neural network did not “see” in training).

## How much data do you need for a convolutional neural network?

– Cross Validated How much data do you need for a convolutional neural network? If I have a convolutional neural network (CNN), which has about 1,000,000 parameters, how many training data is needed (assume I am doing stochastic gradient descent)?

**How many images per class are sufficient for training a CNN?**

Also I suggest if you are using TensorFlow ,read more about GOOGLE’s INCEPTION Image Classifier. It is already trained classifier on google’s image database and you can use it for your images, that way requirements for number of images comes down drastically.