Data
Weather

Weather

active ARFF Publicly available Visibility: public Uploaded 06-08-2023 by Jan van Rijn
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The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that measure different aspects of the instance. In this case there are four attributes: outlook, temperature, humidity, and windy. The outcome is whether to play or not.

5 features

play (target)nominal2 unique values
0 missing
outlooknominal3 unique values
0 missing
temperaturenumeric12 unique values
0 missing
humiditynumeric10 unique values
0 missing
windynominal1 unique values
0 missing

19 properties

14
Number of instances (rows) of the dataset.
5
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
2
Number of numeric attributes.
3
Number of nominal attributes.
40
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.54
Average class difference between consecutive instances.
0
Percentage of missing values.
0.36
Number of attributes divided by the number of instances.
40
Percentage of numeric attributes.
64.29
Percentage of instances belonging to the most frequent class.
60
Percentage of nominal attributes.
9
Number of instances belonging to the most frequent class.
35.71
Percentage of instances belonging to the least frequent class.
5
Number of instances belonging to the least frequent class.
2
Number of binary attributes.

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