Note

Click here to download the full example code

# Multivariate Classification¶

This tutorial provides an example of how to run classification analyses.

## Load & Prepare Data¶

First, let’s load the pain data for this example. We need to create a data object with high and low pain intensities. These labels need to be specified in the dat.Y field as a pandas dataframe. We also need to create a vector of subject ids so that subject images can be held out together in cross-validation.

```
from nltools.datasets import fetch_pain
import numpy as np
import pandas as pd
data = fetch_pain()
high = data[np.where(data.X['PainLevel']==3)[0]]
low = data[np.where(data.X['PainLevel']==1)[0]]
dat = high.append(low)
dat.Y = pd.DataFrame(np.concatenate([np.ones(len(high)),np.zeros(len(low))]))
subject_id = np.concatenate([high.X['SubjectID'].values,low.X['SubjectID'].values])
```

## Classification with Cross-Validation¶

We can now train a brain model to classify the different labels specified in dat.Y. First, we will use a support vector machine with 5 fold cross-validation in which the same images from each subject are held out together. The predict function runs the classification multiple times. One of the iterations uses all of the data to calculate the ‘weight_map’. The other iterations estimate the cross-validated predictive accuracy.

```
svm_stats = dat.predict(algorithm='svm',
cv_dict={'type': 'kfolds','n_folds': 5, 'subject_id':subject_id},
**{'kernel':"linear"})
```

Out:

```
overall accuracy: 1.00
overall CV accuracy: 0.80
threshold is ignored for simple axial plots
```

SVMs can be converted to predicted probabilities using Platt Scaling

```
platt_stats = dat.predict(algorithm='svm',
cv_dict={'type': 'kfolds','n_folds': 5, 'subject_id':subject_id},
**{'kernel':'linear','probability':True})
```

Out:

```
overall accuracy: 1.00
overall CV accuracy: 0.80
threshold is ignored for simple axial plots
```

Standard OLS Logistic Regression.

```
logistic_stats = dat.predict(algorithm='logistic',
cv_dict={'type': 'kfolds','n_folds': 5, 'subject_id':subject_id})
```

Out:

```
overall accuracy: 1.00
overall CV accuracy: 0.75
threshold is ignored for simple axial plots
```

Ridge classification

```
ridge_stats = dat.predict(algorithm='ridgeClassifier',
cv_dict={'type': 'kfolds','n_folds': 5, 'subject_id':subject_id})
```

Out:

```
overall accuracy: 1.00
overall CV accuracy: 0.79
threshold is ignored for simple axial plots
```

## ROC Analyses¶

We are often interested in evaluating how well a pattern can discriminate between different classes of data. However, accuracy could be high because of a highly sensitive but not specific model. Receiver operator characteristic curves allow us to evaluate the sensitivity and specificity of the model. and evaluate how well it can discriminate between high and low pain using We use the Roc class to initialize an Roc object and the plot() and summary() methods to run the analyses. We could also just run the calculate() method to run the analysis without plotting.

```
from nltools.analysis import Roc
roc = Roc(input_values=svm_stats['dist_from_hyperplane_xval'],
binary_outcome=svm_stats['Y'].astype(bool))
roc.plot()
roc.summary()
```

Out:

```
------------------------
.:ROC Analysis Summary:.
------------------------
Accuracy: 0.82
Accuracy SE: 0.11
Accuracy p-value: 0.00
Sensitivity: 0.75
Specificity: 0.89
AUC: 0.88
PPV: 0.88
------------------------
```

The above example uses single-interval classification, which attempts to determine the optimal classification interval. However, sometimes we are intersted in directly comparing responses to two images within the same person. In this situation we should use forced-choice classification, which looks at the relative classification accuracy between two images. You must pass a list indicating the ids of each unique subject.

```
roc_fc = Roc(input_values=svm_stats['dist_from_hyperplane_xval'],
binary_outcome=svm_stats['Y'].astype(bool), forced_choice=subject_id)
roc_fc.plot()
roc_fc.summary()
```

Out:

```
------------------------
.:ROC Analysis Summary:.
------------------------
Accuracy: 1.00
Accuracy SE: 0.19
Accuracy p-value: 0.00
Sensitivity: 0.99
Specificity: 0.99
AUC: 0.99
PPV: 0.99
------------------------
```

**Total running time of the script:** ( 1 minutes 40.527 seconds)