Python sklearn decision tree classifier with multiple features?

Question

I'm trying to run a prediction on training data with four features; my code:

from sklearn.cross_validation import train_test_split

X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.33, random_state=42)

# Train
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)

# Plot the decision boundary
plt.subplot(2, 3, pairidx + 1)

x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
                     np.arange(y_min, y_max, plot_step))

Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)

plt.xlabel(iris.feature_names[pair[0]])
plt.ylabel(iris.feature_names[pair[1]])
plt.axis("tight")

# Plot the training points
for i, color in zip(range(n_classes), plot_colors):
    idx = np.where(y == i)
    plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],
                cmap=plt.cm.Paired)

plt.axis("tight")

plt.suptitle("Decision surface of a decision tree using paired features")
plt.legend()
plt.show()

On my predict line: Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) I get the following error:

Number of features of the model must match the input. Model n_features is 4 and input n_features is 2

The iris data is a 150x4 data set. How do I get this to work for 4 features?

Why are you using ravel(). Are you following any web tutorial? Post the link. What is plot_step, pairidx? — Vivek Kumar
– Vivek Kumar, Commented Mar 22, 2017 at 6:54

Jai · Accepted Answer · 2018-03-06 10:32:28Z

1

During training the number of feature that you provided is 4
But when you are predicting you are providing a sample with 2 features which is to be predicted
The number of feature used in training you have to use the same number of features when you are making a prediction
If you do the following : print(np.c_[xx.ravel(), yy.ravel()]) it will give you a shape of : (30, 2) if your plot_step is 1
The numpy array that you provide to the predict function as argument has to be of shape : (x, 4) where x can be any positive integer but the number of columns in your numpy array has to be necessarily 4

answered Mar 6, 2018 at 10:32

Jai

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