PHPMem v2.0.1
Version
1.6.45
Uptime
17 days 16 hours 43 minutes 17 seconds
Memory
Total
512MB
Used
12,72MB (2.48%)
Free
499,28MB
Keys
Current
14 060
Total (since start)
40 994
Evictions
0
Reclaimed
760
Expired Unfetched
0
Evicted Unfetched
0
Connections
Current
14 / 1 024 max
Total
240 301
Rejected
0
llm:590840c06cd098c5dcfd3327f65c63c3f4a01df8180a51722742474122471878
Edit
**Yes. The records fall into three groups of 50 by `Species`: Iris-setosa, Iris-versicolor and Iris-virginica.** The gold table `Iris_by_Species` shows the groups are perfectly balanced and differ clearly in their measurements.
**Petal size separates the groups best**
- **Setosa is cleanly isolated.** Its petal length runs 1.0–1.9 cm (average 1.46) and its petal width 0.1–0.6 cm (average 0.24). The smallest versicolor petal is 3.0 cm long and 1.0 cm wide, so no setosa overlaps the other two on petal length.
- **Versicolor and virginica are ordered but overlap.**
- Petal length averages 4.26 cm for versicolor (range 3.0–5.1) and 5.55 cm for virginica (range 4.5–6.9).
- Petal width averages 1.33 cm for versicolor (range 1.0–1.8) and 2.03 cm for virginica (range 1.4–2.5).
- The ranges overlap at 4.5–5.1 cm for length and 1.4–1.8 cm for width. A single petal threshold will therefore misclassify some borderline flowers.
**Sepal size separates the groups less well**
- Sepal length averages 5.01 cm for setosa, 5.94 for versicolor and 6.59 for virginica. Versicolor and virginica overlap heavily (4.9–7.0 against 4.9–7.9).
- Sepal width is the weakest separator. Setosa is widest (average 3.42) but its range of 2.3–4.4 overlaps the others, and versicolor (2.77) and virginica (2.97) are nearly indistinguishable.
**What this means for grouping**
- A simple petal-length rule (roughly under 2 cm) isolates setosa.
- Splitting versicolor from virginica needs a combination of petal length and width, and ideally sepal features too. A clustering or multivariate classifier would do this better than one cut-off.
- These conclusions come from the per-species summaries (averages, minimums and maximums). I did not run a clustering algorithm, so I have not tested whether unsupervised methods would recover the same three groups. That is plausible given how well setosa separates, but it is untested here.