Make Predictions from a "DecisionTreeClassifier" Object

Similar to other predict methods, this function predicts fitted values from a fitted "DecisionTreeClassifier" object.

# S3 method for DecisionTreeClassifier
predict(model, data, key, features = NULL, verbose = NULL, thread.ratio = NULL)

Format

S3 methods

Arguments

model

R6Class object
A "DecisionTreeClassifier" object for prediction.

data

DataFrame
DataFrame containting the data.

key

character
Name of the ID column.

features

character of list of characters, optional
Name of feature columns for prediction.
If not provided, it defaults to all non-key columns of data.

verbose

logical , optional
Defaults to FALSE. Valid only for Classification.

thread.ratio

double, optional
Controls the proportion of available threads that can be used by this function.
The value range is from 0 to 1, where 0 indicates a single thread, and 1 indicates all available threads. Values between 0 and 1 will use up to that percentage of available threads.
Values outside the range from 0 to 1 are ignored, and the actual number of threads used is then be heuristically determined.
Defaults to -1.

Value

Dataframe
Predicted values, structured as follows.

  • ID column, with same name and type as data's ID column.

  • SCORE, type NVARCHAR(100), predicted classes.

  • CONFIDENCE, type DOUBLE. Representing the confidence of a class

Examples

Input DataFrame data2:


> data2$Collect()
   ID   OUTLOOK  HUMIDITY  TEMP WINDY
1   0  Overcast      75.0    70   Yes
2   1      Rain      78.0    70   Yes
3   2     Sunny      66.0    70   Yes
4   3     Sunny      69.0    70   Yes
5   4      Rain        NA    70   Yes
6   5      <NA>      70.0    70   Yes
7   6       ***      70.0    70   Yes

Call the function and obtain the result:


> result = predict(dtc, data2, verbose=FALSE)
   ID        SCORE  CONFIDENCE
1   0         Play    1.000000
2   1  Do not Play    1.000000
3   2         Play    1.000000
4   3         Play    1.000000
5   4  Do not Play    1.000000
6   5         Play    0.692308
7   6         Play    0.692308