Learn about kernel density estimation.
- [Instructor] Kernel density estimation…is a process by which we can estimate…the shape of a dataset.…After we have computed the shape of a dataset,…we can compute the probability…in which an event will happen.…So, in this video we're going to introduce…the kernel density estimator.…The kernel density estimator requires a kernel function…and we're going to discuss the requirements…of a kernel function and how the normal distribution…meets those requirements.…Finally, we're going to compute the KDE of a set of values.…
So, kernel density estimation…tries to estimate the shape of a dataset.…All data has a shape,…you could also refer to this as the density…and that shape is not always clear.…Once we have estimated the shape of a dataset…we can compute the probability of a particular observation.…We require a kernel function…and in this video we will use the normal.…There are three requirements of a kernel.…There are several different types of formulas…that meet these requirements…first of which is that a kernel needs to be smooth.…
Note: This course was created by Packt Publishing. We are pleased to host this training in our library.
- Data ranges, means, and medians
- Standard deviation
- SQLite3 command line
- Slices of data
- Regular expressions
- Atoms and modifiers
- Character classes
- Line plots of a single variable
- Plotting a moving average
- Feature scaling
- Scatter plots
- Normal distribution
- Kernel density estimation (KDE)
Skill Level Intermediate
NumPy Data Science Essential Trainingwith Charles Kelly3h 54m Intermediate
SQL: Data Reporting and Analysiswith Emma Saunders2h 16m Intermediate
1. Descriptive Statistics
3. Regular Expressions
5. Kernel Density Estimation
6. Course Review
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