Supervised and unsupervised learning algorithms

In parrot's AI intelligent learning and training models, supervised learning and unsupervised learning algorithms are two commonly used machine learning methods. They have different characteristics and application scenarios when solving problems and processing data. The following is the algorithm used by parrot:

Supervised learning algorithm:

1. Linear Regression: used to establish a linear relationship between features and targets and predict continuous values.

2. Logistic Regression: Used to solve binary classification problems, and the output results are probability values.

3. Decision Trees: Classify and regress data through tree structures.

4. Support Vector Machines (SVM): used to solve classification and regression problems, capable of processing linear and nonlinear data.

5. k-Nearest Neighbors (KNN): Classification or regression based on the nearest neighbors of data points.

6. Neural Networks: Building complex nonlinear models through multiple layers of neurons can solve a variety of problems.

Unsupervised learning algorithm:

1. Clustering: Divide data into different categories. Common algorithms include k-means clustering and hierarchical clustering.

2. Dimensionality Reduction: Reduce the dimensionality of data. Common algorithms include Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE).

3. Association Rule Learning: Discover rules and correlations in data sets. Common algorithms include Apriori algorithm and FP-growth algorithm.

4. Anomaly Detection: Discover outliers in data. Common algorithms include statistical methods and machine learning-based methods.

5. Generative Adversarial Networks (GANs): consists of two networks, a generator network and a discriminator network, used to generate data with similar distributions.

These supervised learning and unsupervised learning algorithms are widely used in AI intelligent learning and training models. Appropriate algorithms can be selected according to specific problems and data characteristics to solve them. In the application of the Parrot platform, according to the needs of quantitative trading and data conditions, appropriate supervised learning or unsupervised learning algorithms are selected for modeling and analysis to achieve better trading decision support.