Listwise vs Pairwise Deletion
Purpose In statistical terms, pairwise deletion and list-wise deletion refer to how missing values are handled when computing statistics or fitting models. How is this done? The deletion process The difference: Pairwise and Listwise Deletion The measure can be anything, the median, correlation coefficient, etc.
Label Encoding
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A Simple Sample Imputation – Quick Way To Learn
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Mean Absolute Percentage Error (MAPE)
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Box-Cox Transformation
Purpose Transforms non-normal data into a normal distribution. How is this done? Where is this used? In training linear regression models. Since the linear regression models assume the normality of its features, this statistical tool comes in very handy to transform a feature with a non-normal distribution into a normal distribution Other uses of it
Exploring the Intuition Behind Log Loss in Machine Learning
Log Loss Log loss, or logarithmic loss, is a generic term that refers to the negative log-likelihood of the true labels given a set of predicted probabilities. Cross Entropy Loss Cross-entropy loss, in a broad sense, measures the difference between two probability distributions. while the terms “Cross-Entropy Loss” and “Log Loss” are used interchangeably, the […]
Decoding Video Jargon: A Simple Guide to MPEG-4, H.264, and VGA Resolutions
Link Between Media Basics and Machine Learning Tasks Gaining a high-level understanding of video and image formats serves as an initial stride in connecting the fundamentals of multimedia with machine learning applications. This understanding is essential for tasks such as video and image analysis, face recognition, activity tracking, and efficient content filtering for explicit or […]
Scaling Decisions: Exploring Feature Scaling Options in Machine Learning
Choosing the appropriate scaling method depends on the characteristics of your data and the requirements of your machine-learning model. Here’s a brief overview of each scaling method and when you might use them: Standard Scaler How does it work? Scales the features to have a mean of 0 and a standard deviation of 1 using […]
SageMaker Checkpointing
Purpose Save the state of ML during training. Usage save the snapshot of the model. restart a training job from this snapshot. analyze the intermediate state of the model during training. Use checkpoints with managed spot instances to save cost. How does it work? Training code runs on the training containers on EC2 instances. Uses […]