1) Describe how you would have used the Naive Bayes algorithm for Project 2 (instead of logistic regression).
If I were to use the Naive Bayes algorithm for Project 2 instead of logistic regression, the following steps could be followed:
1) Data Preprocessing: Begin by performing any necessary data preprocessing steps such as removing any irrelevant columns, handling missing values, and performing any feature scaling or normalization.
2) Data Partitioning: Split the dataset into a training set and a test set. The training set will be used to train the Naive Bayes classifier, while the test set will be used to evaluate its performance.
3) Feature Selection: If needed, perform feature selection to select the most relevant features for classification. This step would help reduce the dimensionality of the dataset and improve the performance of the classifier.
4) Naive Bayes Classifier Training: Train the Naive Bayes classifier using the training dataset. Naive Bayes assumes that the features are conditionally independent, given the class label. In this case, the class label would be the sentiment (positive, negative, or neutral) of the given text. Naive Bayes assumes different probability distributions for continuous and categorical features.
5) Model Evaluation: Evaluate the performance of the trained Naive Bayes classifier using the test dataset. Common evaluation metrics for text classification tasks might include accuracy, precision, recall, or F1-score.
Note: Naive Bayes algorithm is typically used for text classification tasks, where each instance is represented as a document or a set of features. Therefore, it is important to properly represent the text data as features (e.g., using bag-of-words or TF-IDF) before applying the Naive Bayes algorithm.