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KNN can be computationally expensive during prediction as it requires storing all training data, and it is
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sensitive to the scale of the data and the choice of distance metric. Naive Bayes is widely used in text
classification and spam detection tasks. It is based on Bayes’ theorem and assumes conditional
independence among features. While simple and efficient, it may not perform well if the assumption of
feature independence is not realistic in many real-world datasets. Overall, shallow learning models are
highly effective when the data is simple, clean, or when a simple and interpretable model is required.
However, they often struggle with complex, high-dimensional, or nonlinear data, making more powerful
methods such as ensemble learning or deep learning models necessary.

Ensemble learning models
Ensemble learning models combine multiple base models (also known as weak learners) to create a more
powerful and accurate predictive model. These methods are based on the idea that combining the
predictions of several models can outperform any individual model. By aggregating the predictions from
various base models, ensemble methods reduce the risk of overfitting and improve the model’s overall
accuracy . There are two main types of ensemble learning techniques: bagging and boosting. Bagging,
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which stands for bootstrap aggregating, includes models such as random forests and bagging trees [84,85] .
These models are used for classification, regression, and other predictive tasks, particularly concerning high
model variance. Bagging works by training multiple models on different subsets of the data and combining
their predictions, which helps to reduce overfitting and increases the robustness of the model. However, if
the base model is weak, the method can still suffer from high bias, and training multiple models can be
computationally expensive. On the other hand, boosting methods such as gradient boosting machines
(GBM) , AdaBoost , and XGBoost are widely used for solving complex classification and regression
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problems, especially in competitive ML settings. Boosting works by sequentially correcting the errors made
by previous models, often leading to high performance even in challenging tasks. It effectively addresses
both bias and variance, but it can become prone to overfitting if the number of iterations is too large or if
the model is not properly regularized. Another ensemble technique, Stacking , involves combining
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predictions from multiple different models, where each model contributes its unique strengths to the overall
prediction task. This method leverages the diversity of multiple models to create a more powerful predictive
model. While stacking can provide excellent results, it is computationally expensive and requires careful
selection and combination of the models. Ensemble methods are highly powerful because they harness the
diversity of different base models to reduce errors and bias. These techniques are particularly effective when
dealing with noisy, imbalanced, or complex data and are widely used in both research and industry for their
ability to deliver top-tier predictive performance.

Deep learning models
Deep learning models are a powerful class of ML algorithms composed of multiple layers that transform
input features into output predictions. These models are particularly well-suited for capturing complex,
hierarchical data representations, enabling them to model intricate relationships that simpler algorithms
may overlook [90-92] . However, deep learning models typically require large amounts of labeled data and
substantial computational resources, making them ideal for applications in fields that deal with big data. In
the context of optoelectronic materials, deep learning can be leveraged to model the complex relationships
between material properties and performance, which is critical for accelerating material discovery and
design. One of the most common types of deep learning models is artificial neural networks (ANN) .
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ANN is highly flexible and powerful, capable of modeling complex, nonlinear relationships in data.
However, ANN requires large labeled datasets for training, and the computational cost can be significant.
Additionally, without proper regularization, ANN can be prone to overfitting, particularly when working
with limited data or insufficient diversity in material properties. Another type of deep learning model is the