- Causal Analysis for Inferring Cause-Effect Relationships14 min
DoWhy is a Python library that provides a unified framework for causal inference, implementing a four-step methodology: modeling, identification, estimation...
- Choosing the Right Categorical Encoding Technique7 min
Label encoding transforms categorical variables into numerical values by assigning unique integers to each category. This method preserves memory efficiency...
- Choosing the Right Classification Metric11 min
G-Mean (Geometric Mean) measures classification performance when both classes are equally important by calculating the geometric mean of class-wise...
- Comparing MLE and EM Techniques12 min
Maximum Likelihood Estimation (MLE) and Expectation Maximization (EM) are two fundamental techniques in statistical modeling and machine learning. While they...
- Correlation Methods for Data-Driven Decisions7 min
The Pearson correlation coefficient measures the strength and direction of linear relationships between continuous variables, commonly used in financial...
- Correlation Misconceptions and Predictive Power Score8 min
Correlation is a widely used statistical measure, but it's often misunderstood. This slideshow will explore the limitations of correlation and introduce the...
- Correlation vs Causation Avoiding Misinterpretations10 min
Covariance and correlation are statistical measures that describe the relationship between two variables. While they're related concepts, they have distinct...
- Correlation vs Causation Understanding the Difference14 min
Correlation measures the statistical relationship between two variables, indicating how they move together. It quantifies the strength and direction of their...
- Cosine Similarity in Vector Stores7 min
Cosine similarity is a metric used to measure the similarity between two non-zero vectors in a multi-dimensional space. It calculates the cosine of the angle...
- Detecting Anomalies and Outliers in Data10 min
Outliers are data points that significantly differ from other observations. Flagging outliers is crucial for data analysis and model performance.
- Detecting Disruptive Outliers in Data7 min
Outliers are data points that significantly deviate from the general pattern of a dataset. They can profoundly impact statistical analyses and machine...
- Estimating Causal Impact of Late Deliveries on Customer Satisfaction8 min
Propensity Score Matching (PSM) estimates treatment effects by creating matched pairs of treated and control subjects with similar characteristics. The...
- Evaluation Metrics Storytelling for Your Model8 min
Evaluation metrics are crucial tools in machine learning that help us assess model performance. They provide insights into how well our models are performing...
- Feature scaling is NOT always necessary9 min
Feature scaling is a common preprocessing step in machine learning, but it's not always required. In this presentation, we'll explore scenarios where feature...
- Feature Scaling Techniques for Outlier-Robust Data6 min
Feature scaling is crucial when dealing with datasets containing outliers. This presentation explores various techniques to scale features effectively...
- Independent and Dependent Variables in Predictive Modeling8 min
Independent variables are features used as input to predict an outcome. They are the factors that we manipulate or control in an experiment or analysis to...
- Limitations of Binary Classifiers for Face Unlock7 min
A binary classifier is inadequate for a face unlock application due to several inherent limitations. This approach oversimplifies the complex task of facial...
- Limitations of Point Estimates in Regression Modeling13 min
Point estimates from regression models often fail to capture the full complexity of real-world data distributions. While they provide a single value...
- Limitations of Silhouette Score for Arbitrary-Shaped Clusters6 min
The Silhouette score is a widely used metric that measures cluster cohesion and separation, ranging from -1 to 1. While effective for evaluating...
- Market Segmentation in Insurance using Unsupervised ML12 min
Market segmentation is a crucial strategy in the insurance industry, allowing companies to tailor their products and services to specific customer groups. By...
- Mastering Feature Discretization for Non-Linear Modeling14 min
Feature discretization transforms continuous variables into categorical ones through binning. This process enables linear models to capture non-linear...
- Mastering ML Pipelines Common Mistakes to Avoid19 min
Data validation serves as the critical first line of defense in machine learning pipelines, ensuring data quality, consistency, and reliability. Implementing...
- Maximizing Power in Small-Sample Experiments11 min
Small-sample experiments present unique challenges in statistical analysis. This slideshow explores the importance of controlling between-individual...
- Maximum Likelihood Estimation (MLE) and Expectation Maximization (EM)11 min
Maximum Likelihood Estimation (MLE) and Expectation Maximization (EM) are powerful statistical techniques used to estimate parameters in probabilistic...
- Measuring Data Drift with KL Divergence7 min
KL divergence measures the relative entropy between two probability distributions, quantifying how one distribution differs from a reference distribution in...
- Measuring Data Drift with KL Divergence and Wasserstein Distance8 min
Data drift represents the change in data distribution over time that can degrade model performance. Detection requires comparing probability distributions...
- Median A Robust Measure of Central Tendency9 min
The median is a fundamental statistical concept that represents the middle value in a sorted dataset. It's particularly useful when dealing with skewed...
- Misleading Box Plots Limitations and Alternatives7 min
Box plots are powerful tools for visualizing data distributions, but they can sometimes be misleading. A box plot represents only five key values: the...
- Modeling Decision-Making with Linear Ballistic Accumulator9 min
The Linear Ballistic Accumulator (LBA) represents a mathematical framework for modeling decision-making processes where evidence accumulates linearly and...
- Modular ML Pipeline Architecture in 3 Phases14 min
Feature engineering serves as the foundation of any robust machine learning system. This phase handles data preparation, cleaning, and transformation while...