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We can also run a model, like Random Forest and see which are the most important features. So, the statistically significant variables are the: my_important The variable fractal_dimension_worst is statistically significant with a pvalue = 2.3e-15 The variable symmetry_worst is statistically significant with a pvalue = 3e-25 The variable concave points_worst is statistically significant with a pvalue = 2e-124

The variable concavity_worst is statistically significant with a pvalue = 2.5e-72 The variable compactness_worst is statistically significant with a pvalue = 7.1e-55 The variable smoothness_worst is statistically significant with a pvalue = 6.6e-26 The variable area_worst is statistically significant with a pvalue = 2.8e-97 The variable perimeter_worst is statistically significant with a pvalue = 5.8e-119 The variable texture_worst is statistically significant with a pvalue = 1.1e-30 The variable radius_worst is statistically significant with a pvalue = 8.5e-116 The variable fractal_dimension_se is NOT statistically significant The variable symmetry_se is NOT statistically significant The variable concave points_se is statistically significant with a pvalue = 3.1e-24 The variable concavity_se is statistically significant with a pvalue = 8.3e-10

The variable compactness_se is statistically significant with a pvalue = 1e-12 The variable smoothness_se is NOT statistically significant The variable area_se is statistically significant with a pvalue = 5.9e-46 The variable perimeter_se is statistically significant with a pvalue = 1.7e-47 The variable texture_se is NOT statistically significant The variable radius_se is statistically significant with a pvalue = 9.7e-50 The variable fractal_dimension_mean is NOT statistically significant The variable symmetry_mean is statistically significant with a pvalue = 5.7e-16 The variable concave points_mean is statistically significant with a pvalue = 7.1e-116 The variable concavity_mean is statistically significant with a pvalue = 1e-83 The variable compactness_mean is statistically significant with a pvalue = 3.9e-56 The variable smoothness_mean is statistically significant with a pvalue = 1.1e-18

The variable area_mean is statistically significant with a pvalue = 4.7e-88 The variable perimeter_mean is statistically significant with a pvalue = 8.4e-101

The variable texture_mean is statistically significant with a pvalue = 4.1e-25 Print(f'The variable is NOT statistically significant')Īnd we get: The variable radius_mean is statistically significant with a pvalue = 8.5e-96 Since our target is binary, we can compare the values of the independent variables for each group (0,1) by applying t-test. Statistical Significant features with t-test