Update app.py
Browse files
app.py
CHANGED
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@@ -79,7 +79,7 @@ with gr.Blocks(theme=theme) as demo:
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''')
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gr.Markdown("The DecisionTreeClassifier employs a pruning technique that can be configured using the cost complexity parameter, commonly referred to as ccp_alpha."
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" By increasing the value of ccp_alpha, a greater number of nodes can be pruned. This demo demonstrates the impact of ccp_alpha on tree regularization")
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gr.Markdown(
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gr.Markdown("Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. Based on the example from <a href=\"https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py\">scikit-learn</a>")
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test_size = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.2, label="Test size")
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random_state = gr.Slider(minimum=0, maximum=2000, step=1, value=0, label="Random state")
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''')
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gr.Markdown("The DecisionTreeClassifier employs a pruning technique that can be configured using the cost complexity parameter, commonly referred to as ccp_alpha."
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" By increasing the value of ccp_alpha, a greater number of nodes can be pruned. This demo demonstrates the impact of ccp_alpha on tree regularization")
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gr.Markdown('''#### Dataset: Breast Cancer''')
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gr.Markdown("Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. Based on the example from <a href=\"https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py\">scikit-learn</a>")
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test_size = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.2, label="Test size")
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random_state = gr.Slider(minimum=0, maximum=2000, step=1, value=0, label="Random state")
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