In certain cases, the results of fit_transform and transform are not identical.

### Describe the bug

`sklearn.feature_extraction.text.TfidfVectorizer`'s fit_transform and transform methods yield different results in specific scenarios when processing the same text data.

### Steps/Code to Reproduce

In this case, both methods yield identical results.

```py
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer

texts = ["Tokyo is the capital of Japan."]
vectorizer = TfidfVectorizer(max_features=300, ngram_range=(1, 6), analyzer="char")

x = vectorizer.fit_transform(texts)
y = vectorizer.transform(texts)
z = vectorizer.transform(texts)

np.testing.assert_array_equal(y.toarray(), z.toarray())
np.testing.assert_array_equal(x.toarray(), y.toarray())
```
In this case, each method yields a different result.

```py
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer

texts = ["Tokyo is the capital of Japan.", "Tokyo"]  # Tokyo is added at the end of the list.
vectorizer = TfidfVectorizer(max_features=300, ngram_range=(1, 6), analyzer="char")

x = vectorizer.fit_transform(texts)
y = vectorizer.transform(texts)
z = vectorizer.transform(texts)

np.testing.assert_array_equal(y.toarray(), z.toarray())
np.testing.assert_array_equal(x.toarray(), y.toarray())  # AssertionError is raised here
```

### Expected Results

No error is thrown

### Actual Results

```py
in assert_array_compare(comparison, x, y, err_msg, verbose, header, precision, equal_nan, equal_inf, strict)
    793         err_msg += '\n' + '\n'.join(remarks)
    794         msg = build_err_msg([ox, oy], err_msg,
    795                             verbose=verbose, header=header,
    796                             names=('x', 'y'), precision=precision)
--> 797         raise AssertionError(msg)
    798 except ValueError:
    799     import traceback

AssertionError: 
Arrays are not equal

Mismatched elements: 151 / 302 (50%)
Max absolute difference: 1.66533454e-16
Max relative difference: 4.71248267e-16
 x: array([[0.353388, 0.070678, 0.070678, 0.070678, 0.070678, 0.070678,
        0.070678, 0.070678, 0.070678, 0.070678, 0.070678, 0.070678,
        0.070678, 0.070678, 0.070678, 0.070678, 0.070678, 0.070678,...
 y: array([[0.353388, 0.070678, 0.070678, 0.070678, 0.070678, 0.070678,
        0.070678, 0.070678, 0.070678, 0.070678, 0.070678, 0.070678,
        0.070678, 0.070678, 0.070678, 0.070678, 0.070678, 0.070678,...
```

### Versions

```shell
System:
    python: 3.11.4 (main, Aug  9 2023, 14:05:23) [Clang 14.0.3 (clang-1403.0.22.14.1)]
executable: env/bin/python
   machine: macOS-13.4.1-arm64-arm-64bit

Python dependencies:
      sklearn: 1.3.0
          pip: 23.2.1
   setuptools: 65.5.0
        numpy: 1.25.2
        scipy: 1.11.2
       Cython: None
       pandas: 2.0.3
   matplotlib: 3.7.2
       joblib: 1.3.2
threadpoolctl: 3.2.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 10
         prefix: libopenblas
       filepath: env/lib/python3.11/site-packages/numpy/.dylibs/libopenblas64_.0.dylib
        version: 0.3.23.dev
threading_layer: pthreads
   architecture: armv8

       user_api: openmp
   internal_api: openmp
    num_threads: 10
         prefix: libomp
       filepath: env/lib/python3.11/site-packages/sklearn/.dylibs/libomp.dylib
        version: None

       user_api: blas
   internal_api: openblas
    num_threads: 10
         prefix: libopenblas
       filepath: env/lib/python3.11/site-packages/scipy/.dylibs/libopenblas.0.dylib
        version: 0.3.21.dev
threading_layer: pthreads
   architecture: armv8
```

COMMENT:
You are comparing floating point to striclty here:

```python
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer

texts = ["Tokyo is the capital of Japan.", "Tokyo"]  # Tokyo is added at the end of the list.
vectorizer = TfidfVectorizer(max_features=300, ngram_range=(1, 6), analyzer="char")

x = vectorizer.fit_transform(texts)
y = vectorizer.transform(texts)
z = vectorizer.transform(texts)

np.testing.assert_allclose(y.toarray(), z.toarray())
np.testing.assert_allclose(x.toarray(), y.toarray())
```

The difference that you observe is only due to floating point error.

Labels: Bug, Needs Triage