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Class 3: ML Pipelines

Class 3: ML Pipelines#

Goals when using ML#

  1. Understand about the data (data science/ actual science) probability more statistics, maybe fit another examine model parameters, inspect them

  2. understanding about Naive bayes fit different data varies

  3. claims about the learning algorithm run multiple algorithms on the same data possibly multiple data

Basic setup#

  1. test train

  2. training parameters

  3. estimator objects

  4. fit model parameters

  5. metrics

  6. cross validation

import pandas as pd
import seaborn as sns
import numpy as np
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import confusion_matrix, classification_report
from sklearn import datasets

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[1], line 4
      2 import seaborn as sns
      3 import numpy as np
----> 4 from sklearn.model_selection import cross_val_score
      5 from sklearn.model_selection import train_test_split
      6 from sklearn.naive_bayes import GaussianNB

ModuleNotFoundError: No module named 'sklearn'

iris_df = sns.load_dataset('iris')

sns.pairplot(iris_df, hue='species')

X,y = datasets.load_iris(return_X_y=True)

X.shape

y.shape

X_train, X_test, y_train, y_test = train_test_split(X,y,)

gnb = GaussianNB()

gnb.__dict__

gnb.fit(X_train,y_train)

gnb.__dict__

X_test[0]

y_pred = gnb.predict(X_test)

y_pred[:5]

y_test[:5]

confusion_matrix(y_test, y_pred)

gnb.score(X_test,y_test)

gnb2 = GaussianNB(priors=[.5,.25,.25])

gnb2_cv_scores = cross_val_score(gnb2,X_train,y_train)

np.mean(gnb2_cv_scores)

gnb_cv_scores = cross_val_score(gnb,X_train,y_train)

np.mean(gnb_cv_scores)

print(classification_report(y_test,y_pred))

gnb.predict_proba(X_test)
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