Python Multiple linear regression can't plot

Question

I'm trying to run multiple linear regression but i'm having trouble with plotting my results. I'm trying to plot my 3D plot my i get this output ValueError: operands could not be broadcast together with remapped shapes [original->remapped]: (4,) and requested shape (34,)

from sklearn.model_selection import train_test_split
X_train, X_test, y_train,y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)


fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X.iloc[:, 0], X.iloc[:, 1], Y)
ax.plot(X.iloc[:, 0], X.iloc[:, 1], y_pred, color='red')

ax.set_xlabel('Annual Income (k$)')
ax.set_ylabel('Age')
ax.set_zlabel('Spending Score')
plt.show()

Edited:

Edit 2:

ax.plot(X.iloc[:, 0], X.iloc[:, 1], y_pred, color='red') you need to use Y, not y_pred. the shapes of both don't match — Nicolas Gervais
– Nicolas Gervais, Commented Jun 14, 2020 at 17:50
After doing so in my plot my line jumps from one point to another. Is this normal? Edited my post for resulting plot — Arben geze
– Arben geze, Commented Jun 14, 2020 at 17:53
@NicolasGervais what if OP is trying to plot both the predictions and the original values? — M-Wi
– M-Wi, Commented Jun 14, 2020 at 17:56
@M-Wi this is something that i wanted to do as well after figuring out how to plot my results in the first place. — Arben geze
– Arben geze, Commented Jun 14, 2020 at 17:58

Han-Kwang Nienhuys · Accepted Answer · 2020-06-14 19:31:34Z

1

The plot command should be:

ax.plot(X_test.iloc[:, 0], X_test.iloc[:, 1], y_pred, color='red')

because y_pred contains y values only for the subset X_test, not the entire input X.

Plotting with connected lines (ax.plot) doesn't make sense, because the input data is probably not ordered in a meaningful way and the test set is definitely not ordered even if the input data was ordered.

I would plot it like this:

from sklearn.model_selection import train_test_split
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

# generate some data as an example.
np.random.seed(1)
n = 20
X = pd.DataFrame(np.random.uniform(size=(n, 2)), columns=['foo', 'bar'])
Y = X['foo'] + 2*X['bar'] + np.random.normal(scale=0.2, size=n)

X_train, X_test, y_train,y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)


fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X['foo'], X['bar'], Y, label='data')

for x0, x1, yt, yp in zip(X_test['foo'], X_test['bar'], y_test, y_pred):
    ax.plot([x0, x0], [x1, x1], [yt, yp], color='red')

ax.scatter(X_test['foo'], X_test['bar'], y_pred, color='red', marker='s', label='prediction') 

ax.set_xlabel('X0')
ax.set_ylabel('X1')
ax.set_zlabel('y')
ax.legend()
fig.show()

There are other ways to do visualization. You could use np.meshgrid to generate X values on a grid and get y values from your predictor and plot it using plot_wireframe and plot both the train and test data using vertical lines to indicate their vertical distance from the wireframe. It depends on the data what makes sense.

edited Jun 14, 2020 at 19:31

answered Jun 14, 2020 at 18:50

Han-Kwang Nienhuys

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4 Comments

Arben geze Over a year ago

Thanks for your help! When i make the changes you suggested i get this plot (i have edited my post). Again is this normal? Also my X and Y are like this: Y = df0['Spending Score (1-100)'] X = df0[[ 'Annual Income (k$)','Age']] How should i go about changing the X[0], X[1] etc. that you have suggested for comparing predicted data with actual data?

Han-Kwang Nienhuys Over a year ago

I updated the answer. Next time you ask a question, please include input data as well.

Arben geze Over a year ago

Thanks a lot for the help! One question though, why don't all the data points on the plot have their corresponding 'Prediction'? Just like the plot you made in your answer, there are only 3-4 'Predictions' made

Han-Kwang Nienhuys Over a year ago

en.wikipedia.org/wiki/Training,_validation,_and_test_sets

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