Compare algorithms

[Badrgamalahmed]

compare algorithms

from pandas import read_csv from matplotlib import pyplot from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score from sklearn.model_selection import StratifiedKFold from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.naive_bayes import GaussianNB from sklearn.svm import SVC

Load dataset

url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv" names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class'] dataset = read_csv(url, names=names)

Split-out validation dataset

array = dataset.values X = array[:,0:4] y = array[:,4] X_train, X_validation, Y_train, Y_validation = train_test_split(X, y, test_size=0.20, random_state=1, shuffle=True)

Spot Check Algorithms

models = [] models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr'))) models.append(('LDA', LinearDiscriminantAnalysis())) models.append(('KNN', KNeighborsClassifier())) models.append(('CART', DecisionTreeClassifier())) models.append(('NB', GaussianNB())) models.append(('SVM', SVC(gamma='auto')))

evaluate each model in turn

results = [] names = [] for name, model in models: kfold = StratifiedKFold(n_splits=10, random_state=1, shuffle=True) cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy') results.append(cv_results) names.append(name) print('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))

Compare Algorithms

pyplot.boxplot(results, labels=names) pyplot.title('Algorithm Comparison') pyplot.show()

[AbhishekG-1plus]

is this pr still open and maintained?

[Badrgamalahmed]

You can make the addition

[Badrgamalahmed]

Still open

[GaganaMD]

Hey! I would like to be assigned to this issue.