Decision trees are a fundamental tool for every Data Scientist.
But for 3 years, I was hesitant to use them.
In 3 minutes, I'll destroy your confusion. Let's dive in:
1. Decision Tree:
A decision tree is a graphical representation used for decision-making and data analysis. It resembles a tree structure and is commonly used in machine learning, specifically in classification and regression tasks.
2. Structure:
A decision tree consists of nodes and branches. The top node is known as the root node, and it represents the entire dataset.
Decision Nodes: These are where the splits happen, based on a certain condition or attribute.
Leaf/Terminal Nodes: These nodes represent the outcome of the decision process.
Outliers have led me to 100s of business insights. But first I had to find them.
In 3 minutes let me kill your confusion. Let's dive into outliers:
1. Outliers
Outliers or anomalies in a dataset are data points that differ significantly from other observations. They are often important insights signifying key events.
2. Methods: There are 1000s of outlier detection methods. The ones I use can be broken into 4 categories:
1. Statistical 2. Clustering 3. Time Series 4. Machine Learning
Tableau and PowerBI are getting killed by free AI tools.
Case in Point: Microsoft's AI Data Formulator.
100% free in Python. Let's dive in:
1. Data Formulator: Create Rich Visualizations with AI
Data Formulator is an AI-powered tool for data analysts to iteratively create rich visualizations.
Data Formulator is an application from Microsoft Research that uses large language models to transform data, expediting the practice of data visualization.
2. A Novel Approach to Business Intelligence
Unlike most chat-based AI tools where users need to describe everything in natural language, Data Formulator combines user interface interactions (UI) and natural language (NL) inputs for easier interaction. This blended approach makes it easier for users to describe their chart designs while delegating data transformation to AI