The article discusses methods for data scientists to answer 'what if' questions regarding the impact of actions or events without having conducted prior experiments. It focuses on creating counterfactual predictions using machine learning techniques and compares a proposed method with Google's Causal Impact. The approach involves using historical data and control groups to estimate the effect of modifications, addressing challenges such as seasonality, confounders, and temporal drift.
This article provides an overview of feature selection in machine learning, detailing methods to maximize model accuracy, minimize computational costs, and introduce a novel method called History-based Feature Selection (HBFS).
This article provides a non-technical guide to interpreting SHAP analyses, useful for explaining machine learning models to non-technical stakeholders, with a focus on both local and global interpretability using various visualization methods.
A guide on how to use OpenAI embeddings and clustering techniques to analyze survey data and extract meaningful topics and actionable insights from the responses.
The process involves transforming textual survey responses into embeddings, grouping similar responses through clustering, and then identifying key themes or topics to aid in business improvement.
The process involves transforming textual survey responses into embeddings, grouping similar responses through clustering, and then identifying key themes or topics to aid in business improvement.
PCA (principal component analysis) can be effectively used for outlier detection by transforming data into a space where outliers are more easily identifiable due to the reduction in dimensionality and reshaping of data patterns.
A deep dive into time series analysis and forecasting methods, providing foundational knowledge and exploring various techniques used for understanding past data and predicting future outcomes.
A detailed overview of the architecture, Python implementation, and future of autoencoders, focusing on their use in feature extraction and dimension reduction in unsupervised learning.
Support Vector Machine (SVM) algorithm with a focus on classification tasks, using a simple 2D dataset for illustration. It explains key concepts like hard and soft margins, support vectors, kernel tricks, and optimization probles.
This article provides a beginner-friendly introduction to HDBSCAN, a powerful hierarchical clustering algorithm that extends the capabilities of DBSCAN by handling varying densities more effectively. It compares HDBSCAN to DBSCAN and KMeans, highlighting the advantages of HDBSCAN in handling clusters of different shapes and sizes.
This article explains how adding monotonic constraints to traditional ML models can make them more reliable for causal inference, illustrated with a real estate example.
