I have a very large
skewed training set where every feature's
data-points are very similar ?
For example, following is some part of the training data :
93.65034,94.50283,94.6677,94.20174,94.93986,95.21071,1 94.13783,94.61797,94.50526,95.66091,95.99478,95.12608,1 94.0238,93.95445,94.77115,94.65469,95.08566,94.97906,1 94.36343,94.32839,95.33167,95.24738,94.57213,95.05634,1 94.65813,94.65246,94.64984,95.29596,95.14167,95.39941,1 95.50876,94.45346,95.23837,95.26877,94.84924,94.8021,0 94.5774,93.92291,94.96261,95.40926,95.97659,95.17691,0 93.76617,94.27253,94.38002,94.28448,94.19957,94.98924,0
where the last column is the
class-label - only 0 and 1.
This only a part of the dataset, but the actual dataset contains about
95% of samples with
1, and the rest with
0, despite the fact that more or less all the samples are very much similar.
Please suggest an appropriate
scikit-learn) as well.