# Are Strong Baselines Enough? False News Detection with Machine Learning
## 2 Materials and Methods
### Applied Methods
#### 2.3.2 Classifying Methods
For our research, we implemented our MLP classifier with the suggested settings given by Scikit-learn's user guide for neural networks ([[https://scikit-learn.org/stable/modules/neural_networks_supervised.html#classification](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#classification)]([https://scikit-learn.org/stable/modules/neural_networks_supervised.html#classification](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#classification)), accessed on 12 August 2024). MLP has shown good results in the previous research about false news detection [11; 46], and it has been useful for more complex tasks than simple text-based classification [29]. Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression tasks. They have a hierarchical, tree structure that consists of a root node, branches, internal nodes, and leaf nodes. The root node and the internal nodes represent the base problem given by a classification task, e.g., is this article false or true; branches represent the outcome of the problem; and the leaf nodes represent the final decision after calculating all the possible outcomes, e.g., this article is false [47]. DTs are simple and easy to understand, and because of their structure, they are capable of handling multi-output classification problems. For false news detection, DTs have been capable of providing good accuracy results [46; 48]. Boosting in ML refers to a set of ensemble algorithms, such as adaptive boosting (AdaBoost), gradient boosting, or extreme gradient boosting (XGBoost). For this research, we implemented the AdaBoost algorithm. AdaBoost works by training a classifier on a dataset, and the classifier is given a weight according to the performance. AdaBoost gives a higher weight to items that have been classified incorrectly so that the incorrect classification can be corrected. This process is then repeated until the actual values and predicted values reach an acceptable threshold [49]. The AdaBoost algorithm is simple to use, yet it can achieve good accuracy results for false news detection tasks [10]. Stochastic Gradient Descent (SGD) is an algorithm used to minimize the loss functions of a linear classifier model. It is often used in large-scale machine learning problems usually encountered in text classification and natural language processing. SGD is an optimization method, used to train a model to find the optimal set of parameters for the model. In this research, the model we trained using SGD was the \"Modified Huber\" loss function. SGD is efficient and easy to implement, and it works well with large datasets [50]. SGD has been capable of achieving good accuracy results for false-news-detection-related tasks [48]. Passive Aggressive (PA) is an algorithm that is part of a group called linear models, where the target value is expected to be a linear combination of the features [51]. More specifically, PA is used with binary classification tasks, usually when the used data are potentially noisy or they might change over time [52]. Passive aggressive algorithms have been widely used for false-news-detection-related tasks, often with good results [53; 54].