Our top algorithms for predictive modeling

When doing Predictive Models, we create ad hoc algorithms to help our client companies solve specific problems. These algorithms may vary according to the problem that needs solved. In fact, selecting the wrong algorithm will not only result in poor performance, but it may also be a waste of resources. The best way to choose an algorithm is by asking the right questions to the professionals in the industry to identify the exact problem that we are going to solve with the predictive model. That is why we will work in close collaboration with your company experts.

To provide an idea, the top-5 algorithms we use more often for predictive models are:

1. Statistical Models: sophisticated statistical models and approaches such as generalised modeling, regularisation, Bayesian Inference, and time series analysis and forecasting which are used to capture intricate dependencies, model uncertainty, and make robust predictions with generalised models based on complex data distributions and latent structures.
2. Machine Learning Algorithms: powerful models to capture complex data relationships with tree-based, kernel-based, ensemble techniques (bagging, boosting, stacking and blending, and voting ensembles). The advanced supervised ML approaches are enhanced with techniques to improve generalisation and interpretability. Reinforcement learning through environment interactions by applying optimisation of policies, value-based learning, and actor-critic methods are designed for (sequential) decision-making.Advanced and tailored unsupervised learning techniques to focus on discovering hidden patterns, creation of segments and groups are developed and used. These techniques include:
   1. clustering,
   2. dimensionality reduction,
   3. and representation learning.
3. Deep Learning techniques: Deep Learning is based on deep neural networks to learn hierarchical representations of data, being key in applications such as natural language processing and image recognition.
4. Neural Networks are advanced models and approaches from deep learning. Representation learning, and attention-based architectures that enable state-of-the-art and also beyond-state-of-the-art with innovation in areas like computer vision, natural language processing, and sequential modelling. The motivation stands for:
   1. improving generalisation,
   2. scalability,
   3. and interpretability through advanced techniques by pushing the boundaries of what a machine can learn.
5. Explainable Artificial Intelligence (XAI techniques): methods aiming to uncover how models with complex dataset and structure make the predictions, providing transparency in decision-making pipelines and processes. Techniques include both model-agnostic and model-specific approaches; they are crucial to understand the rationale behind a model output and a decision.

→ Check our Predictive Model solutions as well as our Trustworthy AI solutions.