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Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems Read more on this free link
How supply chain business intelligence, data, and analytics can improve the parcel delivery experience: Read more on this free link
While discussing model accuracy, we need to keep in mind the prediction errors, ie: Bias and Variance, that will always be associated with any machine learning model. There will always be a slight difference in what our model predicts and the actual predictions. Read more on this free link
This is a very important topic from an interview point of view. Or some of you working on machine learning models must have experienced the situations when your model gives very good accuracy on training data but gives bad accuracy on testing data. Read more on this free link
A decision tree is a powerful machine learning algorithm extensively used in the field of data science. They are simple to implement and equally easy to interpret. It also serves as the building block for other widely used and complicated machine-learning algorithms like Random Forest, XGBoost, and LightGBM. Read more on this free link
A decision tree is one of the supervised machine learning algorithms. This algorithm can be used for regression and classification problems — yet, is mostly used for classification problems. Read more on this free link
Whenever we discuss model prediction, it’s important to understand prediction errors (bias and variance). There is a tradeoff between a model’s ability to minimize bias and variance. Read more on this free link
What's the optimal machine learning data split ratio and how to achieve it? Learn how to avoid overfitting and start building reliable machine learning models in hours, not weeks.Read more on this free link
A successful and reliable machine learning model has to mature through the various stages. It starts from data collection and wrangling, splitting the data appropriately, Read more on this free link
Clustering is one of the most common exploratory data analysis technique used to get an intuition about the structure of the data. It can be defined as the task of identifying subgroups in the data such that data points in the same subgroup (cluster) are very similar while data points in different clusters are very different. Read more on this free link
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