  Methods. We used different galaxy classification techniques: human labeling,
multi-photometry diagrams, Naive Bayes, Logistic Regression, Support Vector
Machine, Random Forest, k-Nearest Neighbors, and k-fold validation. Results. We
present results of a binary automated morphological classification of galaxies
conducted by human labeling, multiphotometry, and supervised Machine Learning
methods. We applied its to the sample of galaxies from the SDSS DR9 with 0.02 <
z < 0.1 and 24m < Mr < 19.4m. To study the classifier, we used absolute
magnitudes: Mu, Mg, Mr , Mi, Mz, Mu-Mr , Mg-Mi, Mu-Mg, Mr-Mz, and inverse
concentration index to the center R50/R90. Using the Support vector machine
classifier and the data on color indices, absolute magnitudes, inverse
concentration index of galaxies with visual morphological types, we were able
to classify 316 031 galaxies from the SDSS DR9 with unknown morphological
types. Conclusions. The methods of Support Vector Machine and Random Forest
with Scikit-learn machine learning in Python provide the highest accuracy for
the binary galaxy morphological classification: 96.4% correctly classified
(96.1% early E and 96.9% late L types) and 95.5% correctly classified (96.7%
early E and 92.8% late L types), respectively. Applying the Support Vector
Machine for the sample of 316 031 galaxies from the SDSS DR9 at z < 0.1, we
found 141 211 E and 174 820 L types among them.
