Targets_names : array(['malignant', 'benign'], dtype='<U9')
DESCR : breast_cancer_dataset:\n\nBreast cancer wisconsin (diagnostic) dataset
Data Set Characteristics:**\
Number of Instances: 569\n\n
Number of Attributes: 30 numeric, predictive attributes and the class
Attribute Information:\n - radius (mean of distances from center to points on the perimeter)\n - texture (standard deviation of gray-scale values)\n - perimeter\n - area\n - smoothness (local variation in radius lengths)\n - compactness (perimeter^2 / area - 1.0)\n - concavity (severity of concave portions of the contour)\n - concave points (number of concave portions of the contour)\n - symmetry \n - fractal dimension ("coastline approximation" - 1)\n\n The mean, standard error, and "worst" or largest (mean of the three\n largest values) of these features were computed for each image,\n resulting in 30 features. For instance, field 3 is Mean Radius, field\n 13 is Radius SE, field 23 is Worst Radius.\n\n - class:\n - WDBC-Malignant\n - WDBC-Benign\n\n
Missing Attribute Values: None\n\n :Class Distribution: 212 - Malignant, 357 - Benign\n\n
Accuracy Observation 1.Random Forest predicted accuracy: 0.8512585653455618 training set accuracy : 0.9794283216783217 testing set accuracy : 0.8512585653455618 2.Logistic Regression predicted accuracy: 0.9736842105263158 training set accuracy : 0.9868131868131869 testing set accuracy : 0.9736842105263158 3.Naive Bayes predicted accuracy: 0.9736842105263158 training set accuracy : 0.9362637362637363 testing set accuracy : 0.9736842105263158 4.K-Neighbors Classifier predicted accuracy: 0.9473684210526315 training set accuracy : 0.9802197802197802 testing set accuracy : 0.9473684210526315 Conclusion : comparing the above four model based on the breast cancer data set we observed that all the models performs preety much good , from them LR and NB gives approx. 97 % accuracy and Random Forest gives 85 % and K-Neighbor gives 94 % predicted accuracy which is very good . I also calculate the training set as well as testing set accuracy mentioned above.