Learn Data Science, Machine Learning and Artificial Intelligence
In Machine Learning, it is very important to have good understanding of different performance metrics. And it is even more important to know when to use which one to correctly explain the model performance. In classification problems more specific to binary classification, you can not conclude your model without plotting Precision-Recall curve and ROC-AUC curve. In this post, will learn what is the main difference between Precision-Recall curve and ROC-AUC curve and when to use which one.
The ID3 algorithm can be used to construct a decision tree for regression type problems by replacing Information Gain with Standard Deviation Reduction – SDR
A decision tree is built top down from a root node and involves partitioning the data into subsets that contain instances with similar values mean homogeneous data.
Here, standard deviation is used to calculate the homogeneity of a numerical sample (target variable).
What is Covariance coefficient?
Covariance tells you whether two random variables vary with respect to each other or not. And if they vary together then whether they vary in same direction or in opposite direction with respect to each other. So if both random variables vary in same direction then we say it is positive covariance, however if they vary in opposite direction then it is negative covariance.
MLOps is the union of DevOps, machine learning, and data engineering. Built on DevOps’ existing approach, MLOps solutions are developed to increase re-usability, facilitate automation, manage data drift, model versioning, experiment tracking, continuous training and extract richer and consistent insights in a machine learning project.
The main difference between RNN and CNN come from their structure of the Neural Network. Due to their specific design, CNNs are more fit for spatial data such as images whereas RNNs are more for temporal data that comes in sequence.
CNNs employ filters within convolutional layers to transform data. Whereas, RNNs reuse activation functions from other data points in the sequence to generate the next output in a series.
The main difference between RNN and LSTM is in terms of which one maintain information in the memory for the long period of time. Here LSTM has advantage over RNN as LSTM can handle the information in memory for the long period of time as compare to RNN. But the question is what is different in LSTM than RNN by which LSTMs are capable of maintaining long term temporal dependencies (remembering information for long period of time).
Data Science Duniya 