Precision-Recall trade-off

When working with classification tasks, the term Precision-Recall trade-off often appears. Where does it comes from?

Usually, the class with higher probability is chosen to assign the document to. But, what if the maximum probability is equal to 0.1? Should you consider that document to belong to this class with only 10% probability?

The answer varies according to problem at hand. It is possible to add a minimum threshold to accept the classification, and by changing the threshold the values of precision and recall move in opposite directions.

The variables y_true and the model model are loaded. Also, if the probability is lower than the threshold, the document will be assigned to DEFAULT_CLASS (chosen to be class 2).

Este ejercicio forma parte del curso

Recurrent Neural Networks (RNNs) for Language Modeling with Keras

Instrucciones del ejercicio

Use the .predict() method to get the probabilities for each class and store them in the pred_probabilities variable.
Accept the maximum probability only if it is greater than or equal to 0.5 and store the results in the y_pred_50 variable.
Use the np.argmax() and np.max() functions to do the same for a threshold equal to 0.8.
Print the trade_off variable with all the metrics.

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# Get probabilities for each class
pred_probabilities = model.____(X_test)

# Thresholds at 0.5 and 0.8
y_pred_50 = [np.argmax(x) if np.max(x) >= ____ else DEFAULT_CLASS for x in pred_probabilities]
y_pred_80 = [np.____(x) if np.____(x) >= 0.8 else DEFAULT_CLASS for x in pred_probabilities]

trade_off = pd.DataFrame({
    'Precision_50': precision_score(y_true, y_pred_50, average=None), 
    'Precision_80': precision_score(y_true, y_pred_80, average=None), 
    'Recall_50': recall_score(y_true, y_pred_50, average=None), 
    'Recall_80': recall_score(y_true, y_pred_80, average=None)}, 
  index=['Class 1', 'Class 2', 'Class 3'])

____

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Este ejercicio forma parte del curso

Recurrent Neural Networks (RNNs) for Language Modeling with Keras

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In this chapter, you will learn the foundations of Recurrent Neural Networks (RNN). Starting with some prerequisites, continuing to understanding how information flows through the network and finally seeing how to implement such models with Keras in the sentiment classification task.

Exercise 1: Introduction to the course Exercise 2: Comparing the number of parameter of RNN and ANN Exercise 3: Sentiment analysis Exercise 4: Sequence to sequence models Exercise 5: Introduction to language models Exercise 6: Getting used to text data Exercise 7: Preparing text data for model input Exercise 8: Transforming new text Exercise 9: Introduction to RNN inside Keras Exercise 10: Keras models Exercise 11: Keras preprocessing Exercise 12: Your first RNN model

You will learn about the vanishing and exploding gradient problems, often occurring in RNNs, and how to deal with them with the GRU and LSTM cells. Furthermore, you'll create embedding layers for language models and revisit the sentiment classification task.

Exercise 1: Vanishing and exploding gradients Exercise 2: Exploding gradient problem Exercise 3: Vanishing gradient problem Exercise 4: GRU and LSTM cells Exercise 5: GRU cells are better than simpleRNN Exercise 6: Stacking RNN layers Exercise 7: The Embedding layer Exercise 8: Number of parameters comparison Exercise 9: Transfer learning Exercise 10: Embeddings improves performance Exercise 11: Sentiment classification revisited Exercise 12: Better sentiment classification Exercise 13: Using the CNN layer

Next, in this chapter you will learn how to prepare data for the multi-class classification task, as well as the differences between multi-class classification and binary classification (sentiment analysis). Finally, you will learn how to create models and measure their performance with Keras.

Exercise 1: Data pre-processing Exercise 2: Prepare label vectors Exercise 3: Pre-process data Exercise 4: Transfer learning for language models Exercise 5: Transfer learning starting point Exercise 6: Word2Vec Exercise 7: Multi-class classification models Exercise 8: Exploring 20 News Groups dataset Exercise 9: Classifying news articles Exercise 10: Assessing the model's performance Exercise 11: Precision-Recall trade-off

Ejercicio actual

Exercise 12: Precision or Recall, that is the question Exercise 13: Performance on multi-class classification

This chapter introduces you to two applications of RNN models: Text Generation and Neural Machine Translation. You will learn how to prepare the text data to the format needed by the models. The Text Generation model is used for replicating a character's way of speech and will have some fun mimicking Sheldon from The Big Bang Theory. Neural Machine Translation is used for example by Google Translate in a much more complex model. In this chapter, you will create a model that translates Portuguese small phrases into English.

Exercise 1: Sequence to Sequence Models Exercise 2: Text generation examples Exercise 3: NMT example Exercise 4: The Text Generating Function Exercise 5: Predict next character Exercise 6: Generate sentence with context Exercise 7: Change the probability scale Exercise 8: Text Generation Models Exercise 9: Create vectors of sentences and next characters Exercise 10: Preparing the data for training Exercise 11: Creating the text generation model Exercise 12: Neural Machine Translation Exercise 13: Preparing the input text Exercise 14: Preparing the output text Exercise 15: Translate Portuguese to English Exercise 16: Congratulations!