Combining our models (model ensembling/stacking)

[Peco602]

I reviewed the section about model combination in NLP notebook. I think the calculation of baseline_pred_probs is wrong:

# Get mean pred probs for 3 models
baseline_pred_probs = np.max(model_0.predict_proba(val_sentences), axis=1) # get the prediction probabilities from baseline model
combined_pred_probs = baseline_pred_probs + tf.squeeze(model_2_pred_probs, axis=1) + tf.squeeze(model_6_pred_probs)
combined_preds = tf.round(combined_pred_probs/3) # average and round the prediction probabilities to get prediction classes
combined_preds[:20]

It should not be the max value, but simply the second column of the matrix. Hereafter, the version I propose:

# Get mean pred probs for 3 models
baseline_pred_probs = model_0.predict_proba(val_sentences)[:, 1] # get the prediction probabilities from baseline model
combined_pred_probs = baseline_pred_probs + tf.squeeze(model_2_pred_probs, axis=1) + tf.squeeze(model_6_pred_probs)
combined_preds = tf.round(combined_pred_probs/3) # average and round the prediction probabilities to get prediction classes
combined_preds[:20]