- The Iris dataset contains the following data
- 50 samples of 3 different species of iris (150 samples total)
- Measurements: sepal length, sepal width, petal length, petal width
- The format for the data: (sepal length, sepal width, petal length, petal width)
Supervised learning on the iris dataset
- Framed as a supervised learning problem
- Predict the species of an iris using the measurements
- Famous dataset for machine learning because prediction is easy
Machine learning terminology
- Each row is an observation (also known as: sample, example, instance, record)
- Each column is a feature (also known as: predictor, attribute, independent variable, input, regressor, covariate)
Exploring the Iris dataset
Creating a table like look for our data, with the help of Pandas library.
Next step : Visualization of the features. We will plot the combinations of given features in form of scatter to derive the relationship and correlation between the features.
Observation
we observe that the targets are easily differentiated in the above scatter plot.
Petal length and petal width are suitable attributes they have
the ability to predict the output accurately. We also confirm this by
numerical estimation by finding out the correaltion coefficient r
r is very close to 1 which infers that correlation is very strong.
Observation
here we can distinguish among various targets(species of IRIS flower)
but we observe that there exists certain values that can cause confusion
to identify target 1 and 2 as they readily intermix. The values spread over
a range similar between the two.
Analysis
Observe that among the four plotted scatter plots the second one with feature names as petal width and petal length gives a better picture of the relationship with the Species . This plot also shows a strong relationship. We can understand that petal length and petal width can help to predict the target or Species better.
We can also find the correlation between features and Species with the help of heat map. Here we notice that there is a positive correlation between sepal length, petal length, petal width with the species but sepal width has a negative correlation with the species.
The highest correlation can be observed with respect to petal length and petal width. This can also be seen in the above scatter plots.
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