Difference between revisions of "Meteos/Howto"

Revision as of 01:16, 10 February 2017

This section explains what to do to increase the model accuracy.

Under Writing

step 1. Parse Dataset

Parsing dataset is one of the efficient way to increase model accuracy.

You can see example [here].

In this example, user eliminates exception data from dataset using filter method.

step 2. Tuning of model parameters

Parameter tuning is very important to increase the model accuracy. Parameters differs depending on prediction models.

(List of Parameters will be pasted here later.)

A "numIterations" is a common parameter except DecisionTreeModel.

A numIterations is a number of iterations run for each batch of data.

In general, you can increase the model accuracy by specifying larger number in numIterations. However, it takes more time for creating model.

You can specify model parameters in "model_params" section when creating model.

You can see a example [here].

step 3. Evaluate model accuracy

After creating prediction model, you can evaluate model using "meteos model-evaluation-create" method.

Before evaluating model, user have to create datasets for evaluation in advance.

In machine learning, user splits the dataset in general. One is for creating a prediction model, the other is for evaluating model.

step 4. Recreate model with new datasets

it's desirable for prediction models to increase the accuracy continuously.

you can re-create model with new datasets using "meteos model-recreate" method.

If you updated new datasets, you have to recreate a prediction model with new datasets to increase model accuracy continuously.