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removing fixed domains from cls2 plots
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source/classification2.md

Lines changed: 5 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -1301,7 +1301,7 @@ Here we are using the shortcut `point=True` to layer a point and line chart.
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accuracy_vs_k = alt.Chart(accuracies_grid).mark_line(point=True).encode(
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x=alt.X("n_neighbors").title("Neighbors"),
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y=alt.Y("mean_test_score")
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.scale(domain=(0.85, 0.90))
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.scale(zero=False)
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.title("Accuracy estimate")
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)
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@@ -1388,7 +1388,7 @@ large_accuracies_grid = pd.DataFrame(large_cancer_tune_grid.cv_results_)
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large_accuracy_vs_k = alt.Chart(large_accuracies_grid).mark_line(point=True).encode(
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x=alt.X("param_kneighborsclassifier__n_neighbors").title("Neighbors"),
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y=alt.Y("mean_test_score")
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.scale(domain=(0.60, 0.90))
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.scale(zero=False)
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.title("Accuracy estimate")
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)
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@@ -1836,7 +1836,7 @@ plt_irrelevant_accuracies = (
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y=alt.Y(
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"accs",
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title="Model Accuracy Estimate",
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scale=alt.Scale(domain=(0.80, 0.95)),
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scale=alt.Scale(zero=False),
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),
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)
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)
@@ -1899,7 +1899,7 @@ plt_irrelevant_nghbrs_fixed = (
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x=alt.X("ks", title="Number of Irrelevant Predictors"),
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y=alt.Y(
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"Accuracy",
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scale=alt.Scale(domain=(0.75, 0.95)),
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scale=alt.Scale(zero=False),
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),
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color=alt.Color("Type"),
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)
@@ -2140,7 +2140,7 @@ fwd_sel_accuracies_plot = (
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y=alt.Y(
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"accuracy",
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title="Estimated Accuracy",
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scale=alt.Scale(domain=(0.89, 0.935)),
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scale=alt.Scale(zero=False),
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),
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)
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)

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