Machine Learning

Machine learning allows you to automatically find patterns in data and make predictions. Spectrify provides the main techniques for easily analyzing your data.

• PCA (Principal Component Analysis) is great for exploring! It summarizes your data by reducing the number of features you're looking at while keeping the most important information. You can use it to understand groups or interesting patterns.

• Regression Methods are used to predict a specific value, such as a compound percentage, attribute, or any numeric metadata. These methods are based on training with a dataset containing prior known information, which is later used to interpret unknown spectra based on the patterns the machine has learned. PCR (Principal Component Regression) and PLSR (Partial Least Squares Regression) are two types of regression that work well for interpreting your data. PCR is based on an unsupervised approach, while PLSR uses a supervised manner.

• Classification Techniques are perfect for assigning data points to categories or classes. Clustering algorithms like k-means or hierarchical clustering allow you to discover natural groupings in your data. You can use these clusters to classify new data points.

Choose the right tool for your problem! Exploratory techniques like PCA help you understand your data, while regression makes predictions, and classification groups similar data points. Mix and match these techniques to gain the insights you need from your data.