How do ML algorithms work?

Machine learning algorithms work by utilizing data to train a model that can make predictions or decisions without being explicitly programmed. The process involves several key steps, from data preprocessing to model training and evaluation.

Data Preprocessing

Data preprocessing involves cleaning, transforming, and preparing the data for use in the ML algorithm. This step includes handling missing values, encoding categorical variables, and scaling numerical features to ensure the data is suitable for model training.

Model Training

During model training, the algorithm learns patterns and relationships in the data to make accurate predictions or decisions. This process involves feeding the preprocessed data into the algorithm, which adjusts its internal parameters based on the input data to minimize errors or maximize accuracy.

Model Evaluation

After training, the model is evaluated using unseen data to assess its performance and generalization capabilities. Metrics such as accuracy, precision, recall, and F1 score are used to evaluate how well the model performs on new data.

In conclusion, machine learning algorithms work by processing data through steps such as data preprocessing, model training, and evaluation to build predictive models that can make informed decisions or predictions based on input data.