Blog-posts

Creating an optimal model that strikes a balance between underfitting and overfitting requires careful consideration. To assess how well our model performs on new, unseen data, we employ the train-test split technique and cross-validation. Train-Test Split: To evaluate a model’s performance, we split the dataset into a training set (approximately 70-90% of the data) and …

Train-Test split and Cross-validation: Visual Illustrations & Examples Read More »

Confidence intervals (CIs) are a fundamental concept in data science. In this informative guide, we’ll delve into the world of confidence intervals using an intuitive example to help you grasp this concept with confidence. The Bus Stop Scenario: Imagine yourself at a bus stop where the expected arrival time of the bus is usually 9:30 …

Understanding Confidence Intervals with an Intuitive Example Read More »

The Pearson correlation coefficient (denoted as “r”) is widely used in statistics to measure the strength and direction of linear relationships between variables, ranging from -1 (perfect negative linear correlation) to +1 (perfect positive linear correlation). A Pearson correlation coefficient of 0 typically implies the absence of a linear relationship between variables. However, the term …

Non-linear Relationships: When a 0 Pearson Correlation Coefficient Can Be Surprisingly Meaningful Read More »