Suggest scikit-learn instead of sklearn
good catch! _The documentation is not available anymore as the PR was closed or merged._ The test fail is unrelated to this PR and fixed on `main` - merging :) <details>
<summary>Show benchmarks</summary>

PyArrow==6.0.0

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.008942 / 0.011353 (-0.002411) | 0.004617 / 0.011008 (-0.006391) | 0.101310 / 0.038508 (0.062802) | 0.030997 / 0.023109 (0.007888) | 0.306292 / 0.275898 (0.030394) | 0.370533 / 0.323480 (0.047053) | 0.007318 / 0.007986 (-0.000667) | 0.003473 / 0.004328 (-0.000856) | 0.078557 / 0.004250 (0.074307) | 0.036312 / 0.037052 (-0.000740) | 0.308993 / 0.258489 (0.050504) | 0.344411 / 0.293841 (0.050570) | 0.034384 / 0.128546 (-0.094162) | 0.011631 / 0.075646 (-0.064016) | 0.323948 / 0.419271 (-0.095324) | 0.041176 / 0.043533 (-0.002357) | 0.302512 / 0.255139 (0.047373) | 0.322439 / 0.283200 (0.039239) | 0.088955 / 0.141683 (-0.052728) | 1.534918 / 1.452155 (0.082763) | 1.555803 / 1.492716 (0.063087) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.195639 / 0.018006 (0.177633) | 0.423068 / 0.000490 (0.422579) | 0.004101 / 0.000200 (0.003901) | 0.000079 / 0.000054 (0.000025) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023691 / 0.037411 (-0.013721) | 0.100536 / 0.014526 (0.086011) | 0.108399 / 0.176557 (-0.068157) | 0.143515 / 0.737135 (-0.593620) | 0.111886 / 0.296338 (-0.184452) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.417519 / 0.215209 (0.202310) | 4.180463 / 2.077655 (2.102808) | 1.862511 / 1.504120 (0.358391) | 1.658724 / 1.541195 (0.117529) | 1.735847 / 1.468490 (0.267357) | 0.688257 / 4.584777 (-3.896520) | 3.447976 / 3.745712 (-0.297737) | 1.877939 / 5.269862 (-3.391922) | 1.157385 / 4.565676 (-3.408292) | 0.081418 / 0.424275 (-0.342857) | 0.012395 / 0.007607 (0.004788) | 0.518935 / 0.226044 (0.292891) | 5.220355 / 2.268929 (2.951427) | 2.308355 / 55.444624 (-53.136269) | 1.960026 / 6.876477 (-4.916450) | 2.013179 / 2.142072 (-0.128893) | 0.802850 / 4.805227 (-4.002377) | 0.146941 / 6.500664 (-6.353723) | 0.064080 / 0.075469 (-0.011389) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.284443 / 1.841788 (-0.557344) | 13.903755 / 8.074308 (5.829447) | 14.467101 / 10.191392 (4.275709) | 0.156813 / 0.680424 (-0.523611) | 0.028583 / 0.534201 (-0.505618) | 0.406349 / 0.579283 (-0.172934) | 0.413178 / 0.434364 (-0.021186) | 0.491283 / 0.540337 (-0.049055) | 0.571171 / 1.386936 (-0.815765) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.006868 / 0.011353 (-0.004484) | 0.004593 / 0.011008 (-0.006416) | 0.077574 / 0.038508 (0.039066) | 0.027703 / 0.023109 (0.004593) | 0.342096 / 0.275898 (0.066198) | 0.378500 / 0.323480 (0.055020) | 0.005785 / 0.007986 (-0.002201) | 0.003342 / 0.004328 (-0.000986) | 0.076105 / 0.004250 (0.071855) | 0.040369 / 0.037052 (0.003317) | 0.343611 / 0.258489 (0.085122) | 0.391859 / 0.293841 (0.098018) | 0.032675 / 0.128546 (-0.095871) | 0.011623 / 0.075646 (-0.064023) | 0.086623 / 0.419271 (-0.332648) | 0.051955 / 0.043533 (0.008423) | 0.343425 / 0.255139 (0.088286) | 0.368887 / 0.283200 (0.085688) | 0.097117 / 0.141683 (-0.044566) | 1.499546 / 1.452155 (0.047391) | 1.593100 / 1.492716 (0.100383) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.193568 / 0.018006 (0.175562) | 0.409211 / 0.000490 (0.408722) | 0.003797 / 0.000200 (0.003597) | 0.000083 / 0.000054 (0.000029) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.024982 / 0.037411 (-0.012430) | 0.101367 / 0.014526 (0.086841) | 0.108546 / 0.176557 (-0.068010) | 0.144402 / 0.737135 (-0.592733) | 0.112233 / 0.296338 (-0.184105) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.432820 / 0.215209 (0.217611) | 4.341045 / 2.077655 (2.263391) | 2.058326 / 1.504120 (0.554207) | 1.853913 / 1.541195 (0.312718) | 1.942436 / 1.468490 (0.473946) | 0.699130 / 4.584777 (-3.885647) | 3.392879 / 3.745712 (-0.352833) | 1.908277 / 5.269862 (-3.361585) | 1.177998 / 4.565676 (-3.387678) | 0.082700 / 0.424275 (-0.341576) | 0.012505 / 0.007607 (0.004898) | 0.526286 / 0.226044 (0.300242) | 5.279599 / 2.268929 (3.010670) | 2.505771 / 55.444624 (-52.938854) | 2.158460 / 6.876477 (-4.718016) | 2.211437 / 2.142072 (0.069365) | 0.802065 / 4.805227 (-4.003163) | 0.150766 / 6.500664 (-6.349898) | 0.067639 / 0.075469 (-0.007830) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.286595 / 1.841788 (-0.555192) | 13.961894 / 8.074308 (5.887586) | 14.021865 / 10.191392 (3.830473) | 0.164590 / 0.680424 (-0.515834) | 0.016909 / 0.534201 (-0.517292) | 0.392215 / 0.579283 (-0.187069) | 0.408080 / 0.434364 (-0.026284) | 0.488247 / 0.540337 (-0.052090) | 0.575524 / 1.386936 (-0.811412) |

</details>
</details>

![](https://cml.dev/watermark.png#699b0293876015457bfce40f7245d346c34c7717 "CML watermark")

This is kinda unimportant fix but, the suggested `pip install sklearn` does not work.

The current error message if sklearn is not installed:

```
ImportError: To be able to use [dataset name], you need to install the following dependency: sklearn.
Please install it using 'pip install sklearn' for instance.
```
