>>> import pandas_ml as pdml
>>> import sklearn.datasets as datasets

# create ModelFrame instance from sklearn.datasets
>>> df = pdml.ModelFrame(datasets.load_digits())
>>> type(df)
<class 'pandas_ml.core.frame.ModelFrame'>

# binarize data (features), not touching target
>>> df.data = df.data.preprocessing.binarize()
>>> df.head()
   .target  0  1  2  3  4  5  6  7  8 ...  54  55  56  57  58  59  60  61  62  63
0        0  0  0  1  1  1  1  0  0  0 ...   0   0   0   0   1   1   1   0   0   0
1        1  0  0  0  1  1  1  0  0  0 ...   0   0   0   0   0   1   1   1   0   0
2        2  0  0  0  1  1  1  0  0  0 ...   1   0   0   0   0   1   1   1   1   0
3        3  0  0  1  1  1  1  0  0  0 ...   1   0   0   0   1   1   1   1   0   0
4        4  0  0  0  1  1  0  0  0  0 ...   0   0   0   0   0   1   1   1   0   0
[5 rows x 65 columns]

# split to training and test data
>>> train_df, test_df = df.model_selection.train_test_split()

# create estimator (accessor is mapped to sklearn namespace)
>>> estimator = df.svm.LinearSVC()

# fit to training data
>>> train_df.fit(estimator)

# predict test data
>>> test_df.predict(estimator)
0     4
1     2
2     7
...
448    5
449    8
Length: 450, dtype: int64

# Evaluate the result
>>> test_df.metrics.confusion_matrix()
Predicted   0   1   2   3   4   5   6   7   8   9
Target
0          52   0   0   0   0   0   0   0   0   0
1           0  37   1   0   0   1   0   0   3   3
2           0   2  48   1   0   0   0   1   1   0
3           1   1   0  44   0   1   0   0   3   1
4           1   0   0   0  43   0   1   0   0   0
5           0   1   0   0   0  39   0   0   0   0
6           0   1   0   0   1   0  35   0   0   0
7           0   0   0   0   2   0   0  42   1   0
8           0   2   1   0   1   0   0   0  33   1
9           0   2   1   2   0   0   0   0   1  38