## 4 Model Compilation Using Machine Learning Methods
### Classifiers  
Below is a brief description of the machine learning classifiers used:  
Figure 4: Example of normalization by replacing letters. **LINEARR support vector machine (SVC):** Also known as a support vector classifier (SVC), in which each data point is represented as a point in space, and samples from different categories are widely separated. A new sample is first mapped to the space when it arrives. Depending upon which side of the dividing line the new data sample point is on, the new sample is assigned to a group. In the case of binary classification, the classification gap of the SVM can be viewed as a hyperplane. If there are more than two classes, the dividing gap can be seen as a collection of hyperplanes positioned in a high-dimensional region. The optimal hyperplane is chosen to have the maximum possible distance from the nearest sample on each side of the separating hyperplane. 2. **Multimomial Naive Bayes/Bernoulli Naive Bayes:** Naive Bayes is a straightforward learning algorithm that uses the Bayes rule and the basic assumption that, given the class, the attributes are conditionally independent. Even though, in practice, this independence assumption is frequently broken, Naive Bayes frequently yields competitive classification accuracy. Its computational effectiveness and numerous other appealing characteristics contribute to Naive Bayes's widespread use in practice. The multinomial model determines the term frequency or how frequently a term appears in a document. This model's characteristic makes it a viable option for document categorization, given that a phrase could be crucial in determining the sentiment of the content. Additionally, term frequency can be used to determine whether a term will benefit the analysis. In the Bernoulli model, features are independent binary variables that indicate whether a term appears in the document under review. This algorithm, somewhat similar to the multinomial model in the classification process, is also a common method for text classification tasks. 3. **Standard gradient descent (SGD) classifier:** We utilized an assortment of training samples drawn from the probability distribution P (x, y) in supervised learning problems. The association between the input vector x and output label y that we attempted to estimate is represented by the conditional probability P(y | x). A loss function illustrates the discrepancy between the estimated label y and the actual label y. We attempted to quantify the function f that reduces the expected risk. The function f is linearly parameterized by w. The actual gradient of the w vector, commonly approximated as the sum of the gradients induced by each unique training sample, is utilized in standard GD approaches to reduce the empirical risk. SGD \"stochastic gradient descent\" considers one sample at each iteration and updates the weight vector w iteratively using a time-dependent weighting factor. 4. **Decision trees:** These categorize the objects in the dataset by asking questions about the features connected to the data items. Every internal node points to one child node for each potential response to its question and each question is contained within a node. The questions are then encoded as a tree, creating a hierarchy. In its most basic version, yes-or-no questions are asked, with a \"yes\" child and a \"no\" child for each internal node. According to the responses to the item in question, an item is sorted into a class by following the path from the highest node, the root, to a node without children, the leaf. When an object reaches a leaf, it is assigned to the class linked to that leaf. In some instances, every leaf has a probability distribution across the classes to estimate the conditional likelihood that an item will belong to a particular class if it reaches the leaf; however, unbiased probability estimation might be challenging. 5. **Random forest (RF):** This is a common and effective ensemble supervised classification technique.