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Statitstics : The foundation for ML Algorithms
Statistics is the science of collecting, organizing, analyzing, interpreting, and presenting data. It encompasses a wide range of techniques for summarizing data, making inferences, and drawing conclusions.
Data Preprocessing: This includes handling missing values, normalizing or scaling features, encoding categorical variables, and more. Statistical methods such as mean, median, mode, standard deviation, and variance are often used in data preprocessing.
Descriptive Statistics: provides summaries about the sample data and form the basis for understanding the characteristics of the dataset.
Inferential Statistics: such as hypothesis testing, confidence intervals, and regression analysis, help us draw conclusions about the population based on the sample data.
Model Evaluation: Statistics is used to evaluate the performance of machine learning models.
Probability Theory: forms the foundation of many machine learning algorithms, especially in probabilistic models such as Naive Bayes, Hidden Markov Models, and Gaussian Mixture Models.
Regression Analysis: linear regression , logistic regression, polynomial regression, and ridge regression are examples of techniques used in machine learning that have their roots in statistics.
Sampling Techniques: Statistical sampling techniques, such as stratified sampling, random sampling, and cluster sampling, are used to select representative subsets of data for training and testing machine learning models.
Dimensionality Reduction: Techniques like Principal component analysis(PCA) and Singular Value Decomposition (SVD) are used for dimensionality reduction in machine learning
Statistics is a key component of machine learning, with broad applicability in various fields.
Feature engineering relies heavily on statistics to convert geometric features into meaningful predictors for machine learning algorithms.
In image processing tasks like object recognition and segmentation, statistics accurately reflect the shape and structure of objects in images.
Anomaly detection and quality control benefit from statistics by identifying deviations from norms, aiding in the detection of defects in manufacturing processes.
Environmental observation and geospatial mapping leverage statistical analysis to monitor land cover patterns and ecological trends effectively.
Overall, statistics plays a crucial role in machine learning, driving insights and advancements across diverse industries and applications.