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Weather Data

This is the complete dataset used to train and test our prediction model.

Day	Outlook	Temp	Humidity	Wind	Play Tennis?
d1	Sunny	hot	high	weak	no
d2	Sunny	hot	high	strong	no
d3	Overcast	hot	high	weak	yes
d4	Rainy	mild	high	weak	yes
d5	Rainy	cool	normal	weak	yes
d6	Rainy	cool	normal	strong	no
d7	Overcast	cool	normal	strong	yes
d8	Sunny	mild	high	weak	no
d9	Sunny	cool	normal	weak	yes
d10	Rainy	mild	normal	weak	yes
d11	Sunny	mild	normal	strong	yes
d12	Overcast	mild	high	strong	yes
d13	Overcast	hot	normal	weak	yes
d14	Rainy	mild	high	strong	no

How the Model is Trained

The model learns from the data using a process called "supervised learning".

Step 1: Train-Test Split

The dataset is split into two parts: a larger Training Set to teach the model, and a smaller Testing Set to evaluate its accuracy. The blue rows in the table above represent the testing data.

Full Dataset

14 records

→

Training Set

10 records

Testing Set

4 records

Step 2: Training the Random Forest

The model is a Random Forest, which is a collection of many individual Decision Trees. Each tree is trained on a random subset of the training data and features. When making a prediction, all trees "vote", and the majority outcome becomes the final prediction. This makes the model more accurate and robust.

Tree 1

Tree 2

Tree 3

...

Many Trees