What does the array mean in numpy/sklearn datasets? python
arrays, dataset, numpy, python, scikit-learn
Solution
Iris is the well-known Fisher's Iris data set. He measured the length and width of the sepal and petal (two parts of the flower) of three species of Iris. Each row contains the measurements from one flower and there are measurements for 50 flowers of each type, hence the dimensions of iris.data. The actual type of the flower is coded as 0, 1, or 2 in iris.target; you can recover the actual species names (as strings) from iris.target_name.
Fisher showed that his then-new discriminant method could separate the three species based on their sepal and petal measurements and it's been a standard classification data set ever since.
Td;dr: sample data. One example per row with four attributes; 150 examples total. Class labels are stored separately and are coded as integers.
Docs here: http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris
Problem
From the Naive Bayes tutorial in sklearn there's example on `iris` dataset but it looks too cryptic, can someone help to enlighten me? What does the `iris.data` mean? why is there 4 columns? What does the `iris.target` mean? why are they a flat array of 0s, 1s and 2s? ``` from sklearn import datasets iris = datasets.load_iris() print iris.data ``` [out]: ``` [[ 5.1 3.5 1.4 0.2] [ 4.9 3. 1.4 0.2] [ 4.7 3.2 1.3 0.2] [ 4.6 3.1 1.5 0.2] [ 5. 3.6 1.4 0.2] [ 5.4 3.9 1.7 0.4] [ 4.6 3.4 1.4 0.3] [ 5. 3.4 1.5 0.2] [ 4.4 2.9 1.4 0.2] [ 4.9 3.1 1.5 0.1] [ 5.4 3.7 1.5 0.2] [ 4.8 3.4 1.6 0.2] [ 4.8 3. 1.4 0.1] [ 4.3 3. 1.1 0.1] [ 5.8 4. 1.2 0.2] [ 5.7 4.4 1.5 0.4] [ 5.4 3.9 1.3 0.4] [ 5.1 3.5 1.4 0.3] [ 5.7 3.8 1.7 0.3] [ 5.1 3.8 1.5 0.3] [ 5.4 3.4 1.7 0.2] [ 5.1 3.7 1.5 0.4] [ 4.6 3.6 1. 0.2] [ 5.1 3.3 1.7 0.5] [ 4.8 3.4 1.9 0.2] [ 5. 3. 1.6 0.2] [ 5. 3.4 1.6 0.4] [ 5.2 3.5 1.5 0.2] [ 5.2 3.4 1.4 0.2] [ 4.7 3.2 1.6 0.2] [ 4.8 3.1 1.6 0.2] [ 5.4 3.4 1.5 0.4] [ 5.2 4.1 1.5 0.1] [ 5.5 4.2 1.4 0.2] [ 4.9 3.1 1.5 0.1] [ 5. 3.2 1.2 0.2] [ 5.5 3.5 1.3 0.2] [ 4.9 3.1 1.5 0.1] [ 4.4 3. 1.3 0.2] [ 5.1 3.4 1.5 0.2] [ 5. 3.5 1.3 0.3] [ 4.5 2.3 1.3 0.3] [ 4.4 3.2 1.3 0.2] [ 5. 3.5 1.6 0.6] [ 5.1 3.8 1.9 0.4] [ 4.8 3. 1.4 0.3] [ 5.1 3.8 1.6 0.2] [ 4.6 3.2 1.4 0.2] [ 5.3 3.7 1.5 0.2] [ 5. 3.3 1.4 0.2] [ 7. 3.2 4.7 1.4] [ 6.4 3.2 4.5 1.5] [ 6.9 3.1 4.9 1.5] [ 5.5 2.3 4. 1.3] [ 6.5 2.8 4.6 1.5] [ 5.7 2.8 4.5 1.3] [ 6.3 3.3 4.7 1.6] [ 4.9 2.4 3.3 1. ] [ 6.6 2.9 4.6 1.3] [ 5.2 2.7 3.9 1.4] [ 5. 2. 3.5 1. ] [ 5.9 3. 4.2 1.5] [ 6. 2.2 4. 1. ] [ 6.1 2.9 4.7 1.4] [ 5.6 2.9 3.6 1.3] [ 6.7 3.1 4.4 1.4] [ 5.6 3. 4.5 1.5] [ 5.8 2.7 4.1 1. ] [ 6.2 2.2 4.5 1.5] [ 5.6 2.5 3.9 1.1] [ 5.9 3.2 4.8 1.8] [ 6.1 2.8 4. 1.3] [ 6.3 2.5 4.9 1.5] [ 6.1 2.8 4.7 1.2] [ 6.4 2.9 4.3 1.3] [ 6.6 3. 4.4 1.4] [ 6.8 2.8 4.8 1.4] [ 6.7 3. 5. 1.7] [ 6. 2.9 4.5 1.5] [ 5.7 2.6 3.5 1. ] [ 5.5 2.4 3.8 1.1] [ 5.5 2.4 3.7 1. ] [ 5.8 2.7 3.9 1.2] [ 6. 2.7 5.1 1.6] [ 5.4 3. 4.5 1.5] [ 6. 3.4 4.5 1.6] [ 6.7 3.1 4.7 1.5] [ 6.3 2.3 4.4 1.3] [ 5.6 3. 4.1 1.3] [ 5.5 2.5 4. 1.3] [ 5.5 2.6 4.4 1.2] [ 6.1 3. 4.6 1.4] [ 5.8 2.6 4. 1.2] [ 5. 2.3 3.3 1. ] [ 5.6 2.7 4.2 1.3] [ 5.7 3. 4.2 1.2] [ 5.7 2.9 4.2 1.3] [ 6.2 2.9 4.3 1.3] [ 5.1 2.5 3. 1.1] [ 5.7 2.8 4.1 1.3] [ 6.3 3.3 6. 2.5] [ 5.8 2.7 5.1 1.9] [ 7.1 3. 5.9 2.1] [ 6.3 2.9 5.6 1.8] [ 6.5 3. 5.8 2.2] [ 7.6 3. 6.6 2.1] [ 4.9 2.5 4.5 1.7] [ 7.3 2.9 6.3 1.8] [ 6.7 2.5 5.8 1.8] [ 7.2 3.6 6.1 2.5] [ 6.5 3.2 5.1 2. ] [ 6.4 2.7 5.3 1.9] [ 6.8 3. 5.5 2.1] [ 5.7 2.5 5. 2. ] [ 5.8 2.8 5.1 2.4] [ 6.4 3.2 5.3 2.3] [ 6.5 3. 5.5 1.8] [ 7.7 3.8 6.7 2.2] [ 7.7 2.6 6.9 2.3] [ 6. 2.2 5. 1.5] [ 6.9 3.2 5.7 2.3] [ 5.6 2.8 4.9 2. ] [ 7.7 2.8 6.7 2. ] [ 6.3 2.7 4.9 1.8] [ 6.7 3.3 5.7 2.1] [ 7.2 3.2 6. 1.8] [ 6.2 2.8 4.8 1.8] [ 6.1 3. 4.9 1.8] [ 6.4 2.8 5.6 2.1] [ 7.2 3. 5.8 1.6] [ 7.4 2.8 6.1 1.9] [ 7.9 3.8 6.4 2. ] [ 6.4 2.8 5.6 2.2] [ 6.3 2.8 5.1 1.5] [ 6.1 2.6 5.6 1.4] [ 7.7 3. 6.1 2.3] [ 6.3 3.4 5.6 2.4] [ 6.4 3.1 5.5 1.8] [ 6. 3. 4.8 1.8] [ 6.9 3.1 5.4 2.1] [ 6.7 3.1 5.6 2.4] [ 6.9 3.1 5.1 2.3] [ 5.8 2.7 5.1 1.9] [ 6.8 3.2 5.9 2.3] [ 6.7 3.3 5.7 2.5] [ 6.7 3. 5.2 2.3] [ 6.3 2.5 5. 1.9] [ 6.5 3. 5.2 2. ] [ 6.2 3.4 5.4 2.3] [ 5.9 3. 5.1 1.8]] ``` From the `iris.target`, it returns another array of 0s, 1s and 2s. What do they mean? from sklearn import datasets iris = datasets.load_iris() print iris.target [out]: ``` [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2] ```