Logistic Regression with Iris
Content
- Content
- Logistic Regression
- Data
- Iris Species
- Libraries
- Reading Data
- Visualization and Understanding
- Difference analysis between Seabon and species
- Difference analysis between Plotly and species
- Data Editing
- Logistic Regression
- Normalization
- Train Test Split
- Parameter İnitialize
- Sigmoid function
- Cost, gradient
- Update Parameter
- Predict
- Logistic Regression with Math
- Sklearn with LR
- Reference
Logistic Regression
In statistics, the logistic model is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick. This can be extended to model several classes of events such as determining whether an image contains a cat, dog, lion, etc. Wikipedia
Iris Species
The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository.
It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.
The columns in this dataset are:
- Id
- SepalLengthCm
- SepalWidthCm
- PetalLengthCm
- PetalWidthCm
- Species