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Dataset used in the tabular data benchmark, transformed in the same way. This dataset belongs to the "regression on both numerical and categorical features" benchmark. Original link: Original description: Author: City of Seattle Source: - 24-06-2019 Please cite: This data represents crime reported to the Seattle Police Department (SPD). Each row contains the record of a unique event where at least one criminal offense was reported by a member of the community or detected by an officer in the field. This data is the same data used in meetings such as SeaStat ( for strategic planning, accountability and performance management. For more information see: For this version, the task was downsampled to 10 percent. Compute a new target Reported_Time. Compute new date features, ignore some features and encode as features as factor variables.

5 features

ReportedTime (target)numeric29 unique values
0 missing
Precinctnominal5 unique values
0 missing
Sectornominal17 unique values
0 missing
Occurred_hournumeric20 unique values
0 missing
Occurred_minnumeric24 unique values
0 missing

19 properties

Number of instances (rows) of the dataset.
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
Number of missing values in the dataset.
Number of instances with at least one value missing.
Number of numeric attributes.
Number of nominal attributes.
Percentage of binary attributes.
Percentage of instances having missing values.
Percentage of missing values.
Average class difference between consecutive instances.
Percentage of numeric attributes.
Number of attributes divided by the number of instances.
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
Number of binary attributes.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: root_mean_squared_error - target_feature: ReportedTime
Define a new task