Poor performance of KNeighborsClassifier for sklearn version >=0.24.2

### Describe the bug

Hi. I used **sklearn.neighbors.KNeighborsClassifier** in my program but I noticed that its runtime increased when adopting higher versions of  sklearn.

As shown in the following experiment results, this model performs best if we use sklearn <0.24.2.

**My question is that why does such a library version perform better? Are there any issues in newer versions?**

The detailed information is as follows:
Runtime | Memory | Version
-- | -- | --
**451.2495478** | **2337.8830499649** | **1.0.1**
**441.4340003** | **2338.7937555313** | **0.24.2**
26.25025550 | 414.1812286377 | 0.23.2
26.42619310 | 408.6705312729 | 0.22.1
26.42619310 | 408.6052923203 | 0.22
31.0837617 | 409.0471181870 | 0.21.3
29.0283316 | 408.5676021576 | 0.20.3
22.6652508 | 409.7873554230 | 0.19.2

My program only used the apis of  KNeighborsClassifier provided by sklearn.
```Python
knn = KNeighborsClassifier(n_neighbors = 3)
```


### Steps/Code to Reproduce

I create a minor program that used KNeighborsClassifier and you can test it on [colab](https://colab.research.google.com/drive/1GU9Hw8jKObcO5a54-7cNXAA-t9BJuKFi?usp=sharing). The runtime is still higher when adopting the newer sklearn version on this example.

[train.csv](https://drive.google.com/file/d/1f77gSB472ouhZ5bK0ipocBVa3SweGKLc/view?usp=sharing)

Using the above `train.csv` and the following snippet:

```python
# %%
# %%
import pandas as pd

train_data = pd.read_csv("train.csv")
total = train_data.isnull().sum().sort_values(ascending=False)
percent_1 = train_data.isnull().sum() / train_data.isnull().count() * 100
percent_2 = (round(percent_1, 1)).sort_values(ascending=False)
missing_data = pd.concat([total, percent_2], axis=1, keys=["Total", "%"])
cor = train_data.corr()
cor_target = abs(cor["target"])
relevant_features = cor_target[cor_target > 0.5]
s = train_data.dtypes == "object"
train_data_cat_var = list(s[s].index)
train_data.drop(
    ["nom_5", "nom_6", "nom_7", "nom_8", "nom_9", "ord_3", "ord_4", "ord_5"],
    axis=1,
    inplace=True,
)
train_data_cat_var = [
    ele
    for ele in train_data_cat_var
    if ele
    not in ["nom_5", "nom_6", "nom_7", "nom_8", "nom_9", "ord_3", "ord_4", "ord_5"]
]
final_train_data = pd.get_dummies(
    train_data, columns=train_data_cat_var, drop_first=True
)
features = final_train_data.drop(["target"], axis=1).columns
target = final_train_data["target"]

# %%
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
    final_train_data[features], target, test_size=0.2
)

# %%
from sklearn.neighbors import KNeighborsClassifier

# %%
%%time
knn = KNeighborsClassifier(n_neighbors=3, algorithm="brute")
knn.fit(X_train, y_train)
Y_pred_knn = knn.predict(X_test)
```


![image-20220520110041914](https://user-images.githubusercontent.com/26241319/169444004-c50c2e22-489c-4ce9-8771-99bec82693bc.png)


### Expected Results

Similar runtime for the different sklearn versions or better performance for higher sklearn versions.

### Actual Results

According to the above experiment results, when sklearn version is 0.24.2 or 1.0.1, the performance of the model is worse.

### Versions


I test my program with sklearn 1.0.1, 0.24.2, 0.23.2, 0.22.1, 0.22, 0.21.3, 0.20.3 and 0.19.2.

COMMENT:
`X_train.shape` is `(192000, 38)`. This is close to the heuristic limit between tree-based knn and brute force.

Maybe we should re-explore what is a good value for this limit with some systematic benchmarking.

Labels: Bug, module:neighbors, Needs Investigation