43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
"""
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Use a decisiontree classifier to predict flowers based on sepal and petal features
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- This is an example of a supervised ML algorithm
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- it has labels on the training data
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- you tell the model: this is class X during training
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"""
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import matplotlib.pyplot as plt
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from sklearn import datasets
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from sklearn.tree import DecisionTreeClassifier
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from sklearn import tree
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# load the iris data set and look at its dimensions
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iris = datasets.load_iris()
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print(iris.data.size)
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print(iris.target.size)
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print(iris.feature_names)
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print(iris.target_names)
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# use a decision tree classifier
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classifier = DecisionTreeClassifier()
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# use all but the last sample for training
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classifier.fit(iris.data[:-1], iris.target[:-1])
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# use the model to predict the last data sample
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last_sample = iris.data[-1:]
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last_target = iris.target[-1:]
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print(f"predicted: {last_sample} vs real: {last_target}")
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# print the tree for visual inspection
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fig, ax = plt.subplots(figsize=(20, 10))
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tree.plot_tree(
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classifier,
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feature_names=iris.feature_names,
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class_names=iris.target_names,
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filled=True,
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rounded=True,
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ax=ax,
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)
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fig.savefig("decisiontree_iris.png", dpi=150, bbox_inches="tight")
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