In [1]:
%matplotlib inline
In [2]:
# QDA is the generalization of a common technique such as
# quadratic regression. It is simply a generalization of the
# model to allow for more complex models to fit.
In [3]:
# Quadratic Discernment Analysis (QDA)
In [4]:
from sklearn.qda import QDA
qda = QDA()
In [12]:
from sklearn.datasets import make_classification
In [19]:
X, y = make_classification(n_samples=10000,
n_features=100,
n_informative=10)
In [22]:
qda.fit(X, y)
Out[22]:
QDA(priors=None, reg_param=0.0)
In [24]:
predictions = qda.predict(X)
In [25]:
predictions.sum()
Out[25]:
5094
In [26]:
from sklearn.metrics import classification_report
In [28]:
print classification_report(predictions, y)
precision recall f1-score support
0 0.93 0.94 0.94 4906
1 0.95 0.93 0.94 5094
avg / total 0.94 0.94 0.94 10000
In [40]:
from sklearn import cross_validation as cv
import numpy as np
import pandas as pd
In [30]:
import scipy.stats as sp
In [45]:
x_df = pd.DataFrame(X)
for test, train in cv.ShuffleSplit(len(X), n_iter=1):
train_set = X[train]
In [41]:
Out[41]:
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10000 rows × 100 columns
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Content source: DavidBrear/sklearn-cookbook
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