visualize predict_proba for multiclass classification

Heatmaps would be nice way to visualise a 2D matrix. Of-course, if the number of records in your X is large, it is hard to visualize everything in a single go. Probably you have to sample records otherwise. Here I'm showing the visuals for first 10 records, labelling the predicted classes if the predicted probability is greater than 0.1.

Check out this example:

from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np


X, y = make_classification(n_samples=10000,n_features=40,
                           n_informative=30, n_classes=13,
                           n_redundant=0, n_clusters_per_class=1,
                           random_state=42)


X_train, X_test, y_train, y_test = train_test_split(X,y, random_state=42)

forest = RandomForestClassifier(n_estimators=10, random_state=42).fit(X_train, y_train)

pred = forest.predict_proba(X_test)[:10]
fig, ax = plt.subplots(figsize= (20,8))
im = ax.imshow(pred, cmap='Blues')

ax.grid(axis='y')
ax.set_xticklabels([])

ax.set_yticks(np.arange(pred.shape[0]))

plt.ylabel('Records', fontsize='xx-large')
plt.xlabel('Classes', fontsize='xx-large')
fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04) 

for i in range(pred.shape[0]):
    for j in range(13):
        if pred[i, j] >.1:
             ax.text(j, i, j,
                     ha="center", va="center", color="w", fontsize=30)

If your input space is 2D, or if you use some dimensionality reduction technique to embed it in 2D, you could plot the multiclass decision surface:

# generate toy data
X, y = sklearn.datasets.make_blobs(n_samples=1000, centers=13)

# fit classifier
clf = sklearn.ensemble.RandomForestClassifier().fit(X, y)

# create decision surface
xx, yy = np.meshgrid(np.linspace(-13, 12, 100),
                     np.linspace(-13, 12, 100))
Z = clf.predict(np.array([xx.ravel(), yy.ravel()]).T)
Z = Z.reshape(xx.shape)

fig, ax = plt.subplots(1,1, figsize=(8,8))
ax.scatter(X[:,0], X[:,1], c=y, cmap='Paired')
ax.contourf(xx, yy, Z, cmap='Paired', alpha=0.5)

Note this is only shading per label (predict not predict_proba) but you may be able to extend this to shade differently based on the probability.