08 Oct Applications of Statistics in Machine Learning

Statistics provides the tools and methodologies for analyzing data, and making data-driven decisions, forming the pillar of many machine learning processes. Let us see some of the applications of statistics in machine learning:

• Model Evaluation: Using statistical methods to evaluate the performance of machine learning models. For example: accuracy, precision, recall, etc.
• Feature Selection: Identifying the most relevant features for building a predictive model.
• Data Preprocessing: Easily handle missing values, outliers, and normalizing data.
• Algorithm Tuning: Optimizing hyperparameters using statistical methods to improve model performance.
• Uncertainty Quantification: Assessing the uncertainty in model predictions and making probabilistic predictions.

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Read More:

• What is Machine Learning
• What is a Machine Learning Model
• Types of Machine Learning
• Supervised vs Unsupervised vs Reinforcement Machine Learning
• What is Deep Learning
• Feedforward Neural Networks (FNN)
• Convolutional Neural Network (CNN)
• Recurrent Neural Networks (RNN)
• Long short-term memory (LSTM)
• Generative Adversarial Networks (GANs)