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Predicting Rain Tomorrow

The weatherAUS dataset from R's rattle package was used to train a predictive model, predicting the probability of it raining tomorrow based on today's weather observations.

The training dataset consists of daily weather observations from weather stations across Australia, with a target (aka output, predicted or dependent) variable under the column target which has the values of Yes/No indicating if it rained the following day. The input variables in the actual dataset include measurements from today like the amount of sunshine, the humidity at 3pm, the amount of rain recorded, etc.

This MLHub pre-built model uses R's rpart package to build a decision tree as its knowledge representation language. The knowledge is discovered using a so-called recursive partitioning algorithm. A mathematical measure of the information content of the model is used to guide the tree construction. Decision trees are a popular knowledge representation because they are easy to understand and explain.

To install and run the pre-built model:

$ pip install mlhub
$ ml install rain
$ ml configure rain
$ ml demo rain

This model comes from the Essentials of Data Science by Graham Williams https://bit.ly/essentials_data_science. Further support material is also available from https://essentials.togaware.com/.