Value Error when conducting SVM in Python

Question

I am trying to run an SVM linear kernel using a generated dataset. My dataset has 5000 rows and 4 columns:

CL_scaled.head()[screenshot of data frame][1]

I split the data into 20% test and 80% training:

train, test = train_test_split(CL_scaled, test_size=0.2)

and get a shape of (4000,4) for train and (1000,4) for test

However, when I run the svm on the training and testing data, I get the following error:

svclassifier = SVC(kernel='linear', C = 5)
svclassifier.fit(train, test)
ValueError                                Traceback (most recent call last)
<ipython-input-81-4c4a7bdcbe85> in <module>

----> 1 svclassifier.fit(train, test)

~/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py in fit(self, X, y, sample_weight)
    144         X, y = check_X_y(X, y, dtype=np.float64,
    145                          order='C', accept_sparse='csr',
--> 146                          accept_large_sparse=False)
    147         y = self._validate_targets(y)
    148 

~/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)
    722                         dtype=None)
    723     else:
--> 724         y = column_or_1d(y, warn=True)
    725         _assert_all_finite(y)
    726     if y_numeric and y.dtype.kind == 'O':

~/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py in column_or_1d(y, warn)
    758         return np.ravel(y)
    759 
--> 760     raise ValueError("bad input shape {0}".format(shape))
    761 
    762 

ValueError: bad input shape (1000, 4)

Can someone please let me know what is wrong with my code or data? Thanks in advance!

 train.head()
                 0         1             2            3 
    2004    1.619999    1.049560    1.470708    -1.323666
    1583    1.389370    -0.788002   -0.320337   -0.898712
    1898    -1.436903   0.994719    0.326256    0.495565
     892    1.419123    1.522091    1.378514    -1.731400
     4619   0.063095    1.527875    -1.285816   -0.823347

test.head()
            0           1           2         3
1118    -1.152435   -0.484851   -0.996602   1.617749
4347    -0.519430   -0.479388   1.483582    -0.413985
2220    -0.966766   -1.459475   -0.827581   0.849729
 204    1.759567    -0.113363   -1.618555   -1.383653
3578    0.329069    1.151323    -0.652328   1.666561


print(test.shape)
print(train.shape)
(1000, 4)
(4000, 4)

Sociopath · Accepted Answer · 2019-11-11 06:59:31Z

1

The Error is because of train, test = train_test_split(CL_scaled, test_size=0.2)

First thing you need to separate data and output variable and pass it into train_test_split.

# I am assuming your last column is output variable
train_test_split(CL_scaled[:-1], CL_scaled[-1], test_size=0.2).

And train_test_split splits your data into 4 parts X_train, X_test, y_train, y_test

Furthormore, svclassifier.fit takes parameter independent variables and output variable. So you need to pass X_train and y_train

So your code should be

X_train, X_test, y_train, y_test = train_test_split(CL_scaled[:-1], CL_scaled[-1], test_size=0.2)

svclassifier = SVC(kernel='linear', C = 5)

svclassifier.fit(X_train, y_train)

For more details refer documentation

answered Nov 11, 2019 at 6:59

Sociopath

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Comments

PV8 · Accepted Answer · 2019-11-11 12:59:41Z

You are missing the basic concept of supervised machine learning.

In a classification problem you have features X and with them you want to predict a class Y. For example this can look like this:

X                    y
Height Weight        class
170    50            1
180    60            1
10     10            0

The idea for algorithms is that they have a training part (you go to the soccer training to train) and a test part (you test your skills on the field on the weekend).

Therefore your need to split your data, into training and test set.

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(CL_scaled[:-1], CL_scaled[-1], test_size=0.2)

CL_scaled[:-1] is your X, and CL_scalded[-1] is your Y.

Then you are using this to fit your classifier (training part):

svclassifier = SVC(kernel='linear', C = 5)
svclassifier.fit(X_train, y_train)

And then you can test it:

prediction = svcclassifier.predict(X_test, y_test)

This will return your prediction for your test part (y_predict) and you can measure it against your y_test.

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