You can create logistic regression models in a number of ways. In this video, learn how to create a logistic regression model using the Python library scikit-learn and learn how to visualize the predictions for your model using Matplotlib.
- [Instructor] How do you create a logistic regression model … using scikit-learn? … The first thing that you need to know … is that despite the name logistic regression … contain the word regression, … logistic regression is actually a model user classification. … Classification models can be used for tasks … like classifying flower species or image recognition. … All of this of course depends on the availability … and quality of your data. … Logistic regression has some advantages, … model training and predictions are relatively fast, … additionally, no tuning is usually needed for the model. … Finally, it can perform well … with a small number of observations. … In this video, I'll share with you … how you can create a logistic regression model … for binary classification. … The first thing that you need to do … is import the libraries that you want to use. … In this notebook, it's Matplotlib, numpy, seaborn, pandas, … as well as train_test_split, … StandardScaler, and LogisticRegression. …
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- Why use scikit-learn?
- Supervised vs. unsupervised learning
- Linear and logistic regression
- Decision trees and random forests
- K-means clustering
- Principal component analysis (PCA)