sklearn.LogisticRegression ValueError：未知 label 类型：'连续'

Question

I get this error:

ValueError: Unknown label type: 'continuous'

Here is my code:

data=data.dropna()
array = data.values
X = array[:,0:]
y = array[:,-1]
X_train, X_validation, y_train, y_validation = train_test_split(X, Y, test_size=0.20, random_state=1)

models = []
models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
# Evaluate each model in turn
results = []
names = []
for name, model in models:
    # TimeSeries Cross validation
    tscv = TimeSeriesSplit(n_splits=10)
    cv_results = cross_val_score(model, X_train, y_train, cv=tscv, scoring='r2')
    results.append(cv_results)
    names.append(name)
    print('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))
    
# Compare Algorithms
plt.boxplot(results, labels=names)
plt.title('Algorithm Comparison')
plt.show()

I found another post with a similar problem but when I try and fix the problem with:

from sklearn import utils

lab_enc = preprocessing.LabelEncoder()
encoded = lab_enc.fit_transform(y_train)

LogisticRegression and KNeighborsClassifier work, but LinearDiscriminantAnalysis returns nas and the error:

ValueError: The number of samples must be more than the number of classes.

At that point I do not really understand what I am doing, and the documentation doesn't help me much.

Could someone explain these errors to me?

Answer 1

You have used classification models on continous output.

The output must be in the form of 0/1(binary) and 0/1/2/3(for multiclass) for the models you have used.

You can use linear/polynomial/ridge/lasso regression on time series data.

Also you have used X = array[:,0:]. This will include the output column too along with the features(ie X).

Use X = array[:,0:-1]instead. This will exclude the last column when you take the features.