Using the Decision Forest Model with Teradata Python Package

Using the Decision Forest Model with Teradata Python Package - Teradata Python Package

Teradata® Python Package User Guide

Product

Teradata Python Package

Release Number

16.20

Published

February 2020

Language

English (United States)

Last Update

2020-02-29

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B700-4006

lifecycle

Product Category

Teradata Vantage

This section uses the housing data for illustration. The dataset contains 492 samples, each with 12 features describing a home style, and a 13th column indicates the home style.

In this example, you build a Decision Forest model based on the training dataset and apply the model to the test dataset to evaluate the performance of the model.

Import the required modules.

from teradataml.analytics.mle.DecisionForest import DecisionForest

from teradataml.analytics.sqle.DecisionForestPredict import DecisionForestPredict

from teradataml.dataframe.dataframe import DataFrame

from teradataml.data.load_example_data import load_example_data

If the input tables housing_train and housing_test do not already exist, create them and load the datasets into them.
```
load_example_data("decisionforestpredict", ["housing_train","housing_test"])
```
Create a teradataml DataFrame "housing_train" consisting the tokens from the training dataset.
```
housing_train = DataFrame.from_table("housing_train")
```

Train a new Decision Forest model based on the teradataml DataFrame "housing_train" from the training dataset, using the DecisionForest function from teradataml package.

formula = "homestyle ~ driveway + recroom + fullbase + gashw + airco + prefarea + price + lotsize + bedrooms + bathrms + stories + garagepl"

rft_model = DecisionForest(data=housing_train,
                            formula = formula,
                            tree_type="classification",
                            ntree=50,
                            tree_size=100,
                            nodesize=1,
                            variance=0.0,
                            max_depth=12,
                            maxnum_categorical=20,
                            mtry=3,
                            mtry_seed=100,
                            seed=100
                            )

Once the model is created, you can apply the model to the test dataset.

Create a teradataml DataFrame "housing_test" with the tokens from the test dataset.
```
housing_test = DataFrame.from_table("housing_test")
```

Predict the home styles by applying the Decision Forest model to the teradataml DataFrame "housing_test" from the test dataset, using the DecisionForestPredict.

decision_forest_predict_out = DecisionForestPredict(object = rft_model,
                                                    newdata = housing_test,
                                                    id_column = "sn",
                                                    detailed = False,
                                                    terms = ["homestyle"]
                                                    )

Inspect the results.
```
decision_forest_predict_out
```