Computational Informatics
Machine learning: XGBoost
XGBoost, an advanced machine learning algorithm based on gradient boosting, excels in predicting material properties in the field of materials science. By leveraging large-scale experimental and computational data, XGBoost constructs highly accurate surrogate models that capture complex relationships between material features (composition, structure, processing conditions) and their properties (mechanical, thermal, electrical, etc.). Its efficiency, flexibility, and ability to handle large datasets make it an ideal tool for material design, screening, and optimization, significantly enhancing the discovery and development of novel materials.
Get Started >
Machine learning: AdaBoost
AdaBoost, short for Adaptive Boosting, is a machine - learning algorithm that combines multiple weak classifiers to create a strong one. It works by iteratively training classifiers, focusing more on misclassified instances each time. With strong generalization and resistance to overfitting, it's widely used in fields like image recognition and natural language processing. However, it can be sensitive to noisy data and outliers.
Get Started >Machine learning: CatBoost
CatBoost is an open - source gradient boosting algorithm developed by Yandex in 2017. It's designed to handle categorical features efficiently by using innovative techniques like ordered boosting and categorical feature encoding. This makes it highly effective for various data types. CatBoost is known for its strong predictive performance, resistance to overfitting, and ability to work well with default hyperparameters. It's widely used in classification, regression, and ranking tasks.
Get Started >
Machine learning: LightGBM
LightGBM is a gradient boosting framework developed by Microsoft in 2017. It is highly efficient and based on decision tree algorithms. With the ability to handle large datasets and low memory consumption, it is designed for distributed and high - performance machine - learning tasks. LightGBM is widely used in classification, regression, and ranking problems.
Get Started >Machine learning: DecisionTree
Decision Tree is a supervised learning algorithm used for classification and regression tasks. It works by recursively splitting the dataset into subsets based on the values of input features, aiming to create homogeneous groups in terms of the target variable. The tree consists of nodes (representing features), branches (representing decisions) and leaves (representing outcomes). Its advantages include interpretability, handling non - linear relationships and requiring little data preprocessing. However, it can be prone to overfitting, which can be mitigated by techniques like pruning.
Get Started >
Machine learning: RandomForest
Random Forest, proposed by Leo Breiman in 2001, is a widely - used ensemble learning algorithm that constructs multiple decision trees during training. It combines many decision trees to form a powerful model. The algorithm leverages the "wisdom of crowds", where the collective decisions of numerous trees are more accurate and robust than those of individual ones. The process starts with bootstrap sampling, where subsets of the training data are randomly selected with replacement to create diverse datasets for each tree. Then, a decision tree is grown for each subset, and at each node, a random subset of features is chosen for splitting, which reduces the correlation between trees. For a new input, each tree makes a prediction, and the final output is determined by majority voting in classification tasks or averaging in regression tasks. Random Forest offers several advantages. It achieves high accuracy by reducing overfitting and improving generalization through the combination of multiple trees. It's also robust to noise and outliers. Additionally, it can estimate the importance of features. However, it has some drawbacks. It's more computationally intensive and less interpretable compared to single decision trees.
Get Started >Machine learning: DecisionTree
Gaussian Process (GP) is a non - parametric Bayesian approach used for regression and classification tasks. It defines a distribution over functions and is fully specified by its mean function and covariance function (kernel). The kernel encodes prior assumptions about the function, such as smoothness. Gaussian Processes provide probabilistic predictions, outputting not only predictions but also uncertainty estimates. They are flexible, capable of modeling complex functions, and work well with small datasets. However, they can be computationally intensive for large datasets due to their reliance on matrix operations.
Get Started >
Machine learning: SupportVectorMachine
Support Vector Machine (SVM) is a supervised learning algorithm for classification and regression. It works by identifying the optimal hyperplane that best separates different classes in the feature space, aiming to maximize the margin—the distance between the hyperplane and the nearest data points (support vectors). When data isn't linearly separable, kernel functions (e.g., linear, polynomial, radial basis function) are used to map it to a higher - dimensional space. SVM is effective for high - dimensional data, less prone to overfitting than some other algorithms, and versatile in handling various data types due to different kernel functions.
Get Started >