Naive Bayes Review -->

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Naive Bayes Review -->

Naive Bayes Review

BrownFall20/notes/2020-10-16.md

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---

# Class 17: Evaluating Classification and Midsemester Feedback

1. share your favorite rainy day activity (or just say hi) in the zoom chat for attendance
1. log onto prismia

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## Naive Bayes Review


Main assumptions: 
- classification assumes that features will separate the gorups
- NB:  conditionally independent 

```{code-cell} ipython3
# %load http://drsmb.co/310
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB

iris = sns.load_dataset("iris")
iris.head()

X_train, X_test, y_train, y_test = train_test_split(iris.values[:,:4],
                                                    iris.species.values, 
                                                    test_size=0.5, random_state=0)

gnb = GaussianNB()
y_pred = gnb.fit(X_train, y_train).predict(X_test)

y_pred

y_test

sum(y_pred == y_test)

len(y_pred)

gnb.score(X_test, y_test)

71/75

from sklearn.metrics import confusion_matrix, classification_report

confusion_matrix(y_test,y_pred,)

sns.pairplot(data =iris, hue='species')

print(classification_report(y_test,y_pred))

gnb.__dict__

import numpy as np

# %load http://drsmb.co/310
df = pd.DataFrame(np.concatenate([np.random.multivariate_normal(mu, sig*np.eye(4),20)
                                  for mu, sig in zip(gnb.theta_,gnb.sigma_)]))
df['species'] = [ci for cl in [[c]*20 for c in gnb.classes_] for ci in cl]
sns.pairplot(data =df, hue='species')

Reminder to Stop Early for feedback survey

8e29632d844cecb4d94553bbccf1ecf2fa49151d