Question

2.1 Perform 3 types of classification analysis demonstrated in the course. At least one type must be a deep learning model using either a pre-trained or data-trained embedding layer. [64] x = reviews['New_reviewText'] y = reviews['Sentiment'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=321) [55] # Calculate vocab_size from the tokenizer vocab_size = len(tokenizer.word_index) + 1 print(vocab_size) 12163 [] 2.2 Evaluate the models on both the training and testing sets to obtain both performance and goodness of fit.**

          2.1 Perform 3 types of classification analysis demonstrated in the course. At least one type must be a deep learning model using either a pre-trained or data-trained embedding layer.
[64]
x = reviews['New_reviewText']
y = reviews['Sentiment']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=321)
[55] # Calculate vocab_size from the tokenizer
vocab_size = len(tokenizer.word_index) + 1
print(vocab_size)
12163
[]
2.2 Evaluate the models on both the training and testing sets to obtain both performance and goodness of fit.**

2.1 Perform 3 types of classification analysis demonstrated in the course. At least one type must be a deep learning model using either a pre-trained or data-trained embedding layer.
[64]
x = reviews['NewreviewText']
y = reviews['Sentiment']
Xtrain, Xtest, ytrain, ytest = traintestsplit(X, y, testsize=0.25, randomstate=321)
[55] # Calculate vocabsize from the tokenizer
vocabsize = len(tokenizer.wordindex) + 1
print(vocabsize)
12163
[]
2.2 Evaluate the models on both the training and testing sets to obtain both performance and goodness of fit.**

Added by Lauren A.

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