Cross Validation

Geostatistical Analyst のライセンスで利用可能。

サマリー

Removes one data location and predicts the associated data using the data at the rest of the locations. The primary use for this tool is to compare the predicted value to the observed value in order to obtain useful information about some of your model parameters.

Learn more about performing cross validation and validation

使用法

• When using this tool in Python, the result object contains both a feature class and a CrossValidationResult, which has the following properties:

  • Count—Total number of samples used.
  • Mean Error—The averaged difference between the measured and the predicted values.

    

  • Root Mean Square Error—Indicates how closely your model predicts the measured values. The smaller this error, the better.

    

  • Average Standard Error—The average of the prediction standard errors.

    

  • Mean Standardized Error—The average of the standardized errors. This value should be close to 0.

    

  • Root Mean Square Standardized Error—This should be close to 1 if the prediction standard errors are valid. If the root-mean-squared standardized error is greater than 1, you are underestimating the variability in your predictions. If the root-mean-square-standardized error is less than 1, you are overestimating the variability in your predictions.

    

  Only the Mean and Root Mean Square Error results are available for IDW, Global Polynomial Interpolation, Radial Basis Functions, Diffusion Interpolation With Barriers, and Kernel Interpolation With Barriers.

• The fields in the optional output feature class are described in the GA Layer To Points tool.

構文

CrossValidation_ga (in_geostat_layer, {out_point_feature_class})

パラメーター	説明	データ タイプ
in_geostat_layer	The geostatistical layer to be analyzed.	Geostatistical Layer
out_point_feature_class (オプション)	Stores the cross-validation statistics at each location in the geostatistical layer.	Feature Class

派生した出力

名前	説明	データ タイプ
count
mean_error
root_mean_square
average_standard
mean_standardized
root_mean_square_standardized
percent_in_90_interval
percent_in_95_interval
average_crps

コードのサンプル

CrossValidation example 1 (Python window)

Perform cross validation on an input geostatistical layer.

import arcpy
arcpy.env.workspace = "C:/gapyexamples/data"
cvResult = arcpy.CrossValidation_ga("C:/gapyexamples/data/kriging.lyr")
print "Root Mean Square error = " + str(cvResult.rootMeanSquare)

CrossValidation example 2 (stand-alone script)

Perform cross validation on an input geostatistical layer.

# Name: CrossValidation_Example_02.py
# Description: Perform cross validation on an input geostatistical layer.
# Requirements: Geostatistical Analyst Extension

# Import system modules
import arcpy

# Set environment settings
arcpy.env.workspace = "C:/gapyexamples/data"

# Set local variables
inLayer = "C:/gapyexamples/data/kriging.lyr"

# Check out the ArcGIS Geostatistical Analyst extension license
arcpy.CheckOutExtension("GeoStats")

# Execute CrossValidation
cvResult = arcpy.CrossValidation_ga(inLayer)
print "Root Mean Square error = " + str(cvResult.rootMeanSquare)

環境

• 現在のワークスペース
• 地理座標系変換
• 出力座標系

ライセンス情報

• ArcGIS Desktop Basic: 次のものが必要 Geostatistical Analyst
• ArcGIS Desktop Standard: 次のものが必要 Geostatistical Analyst
• ArcGIS Desktop Advanced: 次のものが必要 Geostatistical Analyst