net_worths = numpy.reshape( numpy.array(net_worths), (len(net_worths), 1))
from sklearn.cross_validation import train_test_split
ages_train, ages_test, net_worths_train, net_worths_test = train_test_split(ages, net_worths, test_size=0.1, random_state=42)
### fill in a regression here! Name the regression object reg so that
### the plotting code below works, and you can see what your regression looks like
import sys
if '..\\regression' not in sys.path:
sys.path.append('..\\regression')
from regressionLM import regressionLM
reg, time_train, time_pred = regressionLM(ages_train, net_worths_train, ages_test)
print reg.coef_
try:
plt.plot(ages, reg.predict(ages), color="blue")
except NameError:
pass
plt.scatter(ages, net_worths)
plt.show()
### identify and remove the most outlier-y points
### training-testing split needed in regression, just like classification
from sklearn.cross_validation import train_test_split
feature_train, feature_test, target_train, target_test = train_test_split(features, target, test_size=0.5, random_state=42)
train_color = "b"
test_color = "r"
### Your regression goes here!
### Please name it reg, so that the plotting code below picks it up and
### plots it correctly. Don't forget to change the test_color above from "b" to
### "r" to differentiate training points from test points.
from regressionLM import regressionLM
reg, train_time, test_time = regressionLM(feature_train, target_train, feature_test)
### draw the scatterplot, with color-coded training and testing points
import matplotlib.pyplot as plt
for feature, target in zip(feature_test, target_test):
plt.scatter( feature, target, color=test_color )
for feature, target in zip(feature_train, target_train):
plt.scatter( feature, target, color=train_color )
### labels for the legend
