Implementing SVMs in catalysis research involves several steps:
Data Collection: Gather relevant data from experiments or simulations. Data Preprocessing: Clean and preprocess the data, including normalization or standardization. Feature Selection: Identify and select relevant features that will be used as inputs for the SVM. Model Training: Train the SVM using the training dataset, choosing an appropriate kernel and tuning hyperparameters. Model Evaluation: Evaluate the SVM model using a separate testing dataset to assess its performance.
