## 4 Evaluation
### Baseline  
We considered SVM, RF, XGBoost, RNN, CNN, and MentalBERT as baseline models since they have been used in several works [10; 11; 12; 13; 14; 21] for depression classification.  
* **SVM**[16] analyzes the training data and identifies the separation between the support vectors and the optimal hyperplane (i.e., two classes for binary classification). SVM was configured using a linear kernel.
* **RF**[15] is an ensemble ML algorithm that combines many decision tree predictors and aggregates their outputs to achieve more accurate and robust predictions. This algorithm was configured with 100 estimators, a maximum tree depth of 150, and a random state equal to 42 to ensure reproducibility.
* **XGBoost**[17] is an optimized distributed gradient boosting algorithm designed for efficient and scalable ML model training, combining predictions from multiple weak models to produce a more robust prediction. This algorithm was trained with a learning rate equal to 0.1, a maximum tree depth of 15, and 150 estimators.
* **RNNs**[18] are feedback-connected neural networks, which allows them to have internal states. These internal states provide a memory that can hold information about previous inputs. We set up an LSTM with embedding and dense layers, an Adam optimizer with a learning rate equal to 0.001, and a BinaryCrossEntropyLoss function. The LSTM included 64 neurons, dropout equal to 0.5, a first dense layer with 32 neurons, a second with 8, a third with 2 (all using Tanh activation function), and the last layer with a single neuron with a Sigmoid activation function.
* **CNN**[19] has convolutional and pooling layers. Each convolutional layer computes dot products between the weights of output neurons called filters and a local region in the input volume of the layer to which they are connected. A pooling layer is placed between the convolutional layers to reduce the number of parameters and computations. We trained a CNN with an embedding, Conv1D, dense layer, Adam optimizer with a learning rate equal to 0.001, and BinaryCrossEntropyLoss function. The Conv1D layer included 64 neurons activated with the Tanh function. Before the dense layer, a dropout equal to 0.5 was set up to prevent overfitting during training. The first dense layer included 32 neurons, the second 8, and the third 2 with Tanh activation function. The last layer used a single neuron with a Sigmoid activation function.
* **MentalBERT**[47] is a model based on the encoder-only Transformer architecture proposed for detecting mental illnesses like depression, anxiety, and suicide. Notably, as this model was pre-trained using data related to mental health, we fine-tuned it with the depression-oriented dataset, Adam optimizer, and a learning rate of \(1.5\times 10^{-4}\).  
We used the Scikit-learn 1.4.1 version to construct RF and SVM models and the Xgboost 2.0.3 version library to build the XGBoost model. The tweets were tokenized in SVM, RF,and XGBoost using the Term Frequency-Inverse Document Frequency (TF-IDF). The RNN and CNN models were developed using the Tensorflow 2.15.0 and Keras 2.15.0 versions. These models were tokenized as DEENT-Generic was. MentalBERT was built using the Torch 2.5.1 + cu124 version and its internal tokenizer. Remarkably, all models, including DEENT, were compiled using the T4 Google Colab machine. This GPU machine operated with 16 GB of GDDR6 memory and 2560 CUDA cores.