[MRG+2] logit -> logistic in plot_logistic.py and minor visual improvements to the plot. (scikit-learn#7730)

* Small correction of logit->logistic

The function `1 / (1 + np.exp(-x))` is the logistic function. 
The logit function is the inverse of the logistic function : `log(x/(1-x))`

* Add axes ticks, legend and colors to the plot

* Fix flake8 errors

* Rename legend labels as per review

* Fix pep8 error

Author: Deborah-Digges

examples/linear_model/plot_logistic.py

@@ -4,12 +4,12 @@
 
 """
 =========================================================
-Logit function
+Logistic function
 =========================================================
 
-Show in the plot is how the logistic regression would, in this
+Shown in the plot is how the logistic regression would, in this
 synthetic dataset, classify values as either 0 or 1,
-i.e. class one or two, using the logit-curve.
+i.e. class one or two, using the logistic curve.
 
 """
 print(__doc__)
@@ -48,7 +48,7 @@
 def model(x):
     return 1 / (1 + np.exp(-x))
 loss = model(X_test * clf.coef_ + clf.intercept_).ravel()
-plt.plot(X_test, loss, color='blue', linewidth=3)
+plt.plot(X_test, loss, color='red', linewidth=3)
 
 ols = linear_model.LinearRegression()
 ols.fit(X, y)
@@ -57,9 +57,10 @@ def model(x):
 
 plt.ylabel('y')
 plt.xlabel('X')
-plt.xticks(())
-plt.yticks(())
+plt.xticks(range(-5, 10))
+plt.yticks([0, 0.5, 1])
 plt.ylim(-.25, 1.25)
 plt.xlim(-4, 10)
-
+plt.legend(('Logistic Regression Model', 'Linear Regression Model'),
+           loc="lower right", fontsize='small')
 plt.show()