ml16_ex.py

#For digits dataset in sklearn.dataset, please try following
#classifiers and find out the one that gives best performance. Also find the optimal parameters for that classifier.
from sklearn import datasets
digits=datasets.load_digits()


from sklearn import svm
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import MultinomialNB
from sklearn.tree import DecisionTreeClassifier

model_params = {
    'svm': {
        'model': svm.SVC(gamma='auto'),
        'params': {
            'C': [1, 10, 20],
            'kernel': ['rbf', 'linear']
        }
    },
    'random_forest': {
        'model': RandomForestClassifier(),
        'params': {
            'n_estimators': [1, 5, 10]
        }
    },
    'logistic_regression': {
        'model': LogisticRegression(solver='liblinear', multi_class='auto'),
        'params': {
            'C': [1, 5, 10]
        }
    },
    'naive_bayes_gaussian': {
        'model': GaussianNB(),
        'params': {}
    },
    'naive_bayes_multinomial': {
        'model': MultinomialNB(),
        'params': {}
    },
    'decision_tree': {
        'model': DecisionTreeClassifier(),
        'params': {
            'criterion': ['gini', 'entropy'],

        }
    }
}

from sklearn.model_selection import GridSearchCV
import pandas as pd
scores=[]
for model_name,mp in model_params.items():
    clf=GridSearchCV(mp['model'],mp['params'],cv=5,return_train_score=False)
    clf.fit(digits.data,digits.target)
    scores.append({
        'model':model_name,
        'best_score':clf.best_score_,
        'best_params':clf.best_params_
    })

df=pd.DataFrame(scores,columns=['model','best_score','best_params'])
print(df)