print('OvA:', cv_scores_ova.mean())
plt.figure(figsize=(6,4))
plt.boxplot([cv_scores_ova, cv_scores_ovo])
plt.xticks([1, 2], ['One vs All', 'One vs One'])
plt.title('Prediction: accuracy score')
plt.show()
plt.savefig("/projects/niblab/nilearn_projects/multi-class_strats_box_whisker_plot", bbox_inches = "tight" )
from sklearn.metrics import confusion_matrix
from nilearn.plotting import plot_matrix
svc_ovo.fit(X[session < 1], y[session < 1])
y_pred_ovo = svc_ovo.predict(X[session >= 1])
plot_matrix(confusion_matrix(y_pred_ovo, y[session >= 1]),labels=unique_conditions, cmap='plasma')
plt.title('Confusion matrix: One vs One')
plt.show()
plt.savefig("/projects/niblab/nilearn_projects/multi-class_strats_confusion_matrix_OvO", bbox_inches = "tight" )
svc_ova.fit(X[session < 1], y[session < 1])
y_pred_ova = svc_ova.predilsct(X[session >= 1])
plot_matrix(confusion_matrix(y_pred_ova, y[session >= 1]),
labels=unique_conditions, cmap='plasma')
plt.title('Confusion matrix: One vs All')
plt.show()
plt.savefig("/projects/niblab/nilearn_projects/multi-class_strats_confusion_matrix_OvA", bbox_inches = "tight" )
