Experiments

</>Python

import pandas as pd
from sklearn.datasets import load_breast_cancer

</>Python

data = load_breast_cancer()

df = pd.DataFrame(data.data, columns=data.feature_names)
df["target"] = data.target

display(pd.DataFrame({"rows": [df.shape[0]], "columns": [df.shape[1]]}))
display(pd.DataFrame({"column": df.columns}))
df.head()

	rows	columns
0	569	31

	column
0	mean radius
1	mean texture
2	mean perimeter
3	mean area
4	mean smoothness
5	mean compactness
6	mean concavity
7	mean concave points
8	mean symmetry
9	mean fractal dimension
10	radius error
11	texture error
12	perimeter error
13	area error
14	smoothness error
15	compactness error
16	concavity error
17	concave points error
18	symmetry error
19	fractal dimension error
20	worst radius
21	worst texture
22	worst perimeter
23	worst area
24	worst smoothness
25	worst compactness
26	worst concavity
27	worst concave points
28	worst symmetry
29	worst fractal dimension
30	target

	mean radius	mean texture	mean perimeter	mean area	mean smoothness	mean compactness	mean concavity	mean concave points	mean symmetry	mean fractal dimension	...	worst texture	worst perimeter	worst area	worst smoothness	worst compactness	worst concavity	worst concave points	worst symmetry	worst fractal dimension
0	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.3001	0.14710	0.2419	0.07871	...	17.33	184.60	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890
1	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.0869	0.07017	0.1812	0.05667	...	23.41	158.80	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902
2	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.1974	0.12790	0.2069	0.05999	...	25.53	152.50	1709.0	0.1444	0.4245	0.4504	0.2430	0.3613	0.08758
3	11.42	20.38	77.58	386.1	0.14250	0.28390	0.2414	0.10520	0.2597	0.09744	...	26.50	98.87	567.7	0.2098	0.8663	0.6869	0.2575	0.6638	0.17300
4	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.1980	0.10430	0.1809	0.05883	...	16.67	152.20	1575.0	0.1374	0.2050	0.4000	0.1625	0.2364	0.07678

5 rows × 31 columns

</>Python

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

X = df.drop(columns="target")
y = df["target"]

X_train, X_test, y_train, y_test = train_test_split(
    X, y,
    test_size=0.2,
    stratify=y,
    random_state=42
)

rf = RandomForestClassifier(n_estimators=300, random_state=42)
rf.fit(X_train, y_train)

y_pred = rf.predict(X_test)

report_df = pd.DataFrame(
    classification_report(
        y_test,
        y_pred,
        target_names=data.target_names,
        output_dict=True
    )
).T

report_df.round(3)

	precision	recall	f1-score	support
malignant	0.929	0.929	0.929	42.000
benign	0.958	0.958	0.958	72.000
accuracy	0.947	0.947	0.947	0.947
macro avg	0.943	0.943	0.943	114.000
weighted avg	0.947	0.947	0.947	114.000

</>Python

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import RandomizedSearchCV, StratifiedKFold
from sklearn.metrics import accuracy_score
import pandas as pd

param_dist = {
    "n_estimators": [100, 200, 300, 500, 800],
    "max_depth": [None, 5, 10, 15, 20, 30],
    "min_samples_split": [2, 5, 10, 15],
    "min_samples_leaf": [1, 2, 4, 6],
    "max_features": ["sqrt", "log2", None],
    "bootstrap": [True, False]
}

cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)

rf_search = RandomizedSearchCV(
    estimator=RandomForestClassifier(random_state=42),
    param_distributions=param_dist,
    n_iter=25,
    scoring="accuracy",
    cv=cv,
    n_jobs=-1,
    random_state=42
)

rf_search.fit(X_train, y_train)

best_rf = rf_search.best_estimator_
y_pred_tuned = best_rf.predict(X_test)

tuning_summary = pd.DataFrame([{
    "best_cv_accuracy": rf_search.best_score_,
    "test_accuracy": accuracy_score(y_test, y_pred_tuned),
    **rf_search.best_params_
}]).round(4)

tuning_summary

	best_cv_accuracy	test_accuracy	n_estimators	min_samples_split	min_samples_leaf	max_features	max_depth	bootstrap
0	0.9604	0.9561	200	2	2	sqrt	None	True

</>Python

from sklearn.neighbors import KNeighborsClassifier
# initialize k-Nearest Neighbors
knn = KNeighborsClassifier(n_neighbors=5, metric="euclidean", n_jobs=-1)
# display model card
knn

Export results to csv

Thinking

Crafting code to export results

I need to respond with the current state of the notebook, focusing on exporting results to CSV. The user likely wants to export existing tables like report_df and tuning_summary. I’ll provide minimal code to save these as CSV files in one block. I should include the filenames clearly without asking for clarifications since the user mentioned the outputs were ambiguous. I’ll make sure the code is runnable and concise, focusing only on the relevant results.

You can save the two result tables already in memory as CSV files:

</>Save output files

report_df.to_csv("classification_report.csv", index=True)
tuning_summary.to_csv("random_forest_tuning_summary.csv", index=False)

pd.DataFrame({
    "file": ["classification_report.csv", "random_forest_tuning_summary.csv"]
})

	file
0	classification_report.csv
1	random_forest_tuning_summary.csv

The export worked: the latest notebook output confirms two CSV files were created.

classification_report.csv
random_forest_tuning_summary.csv

This matches the results currently in memory:
- report_df was saved with its index included, which preserves the report row labels.
- tuning_summary was saved without the index, which is appropriate for a one-row summary table.

How can I use the AutoML feature of MLJAR?

Thinking

Setting up AutoML

I should start by installing the library and then verifying the import. Since the user wants to know how to use it, I can begin with the setup in a concise manner. It might be useful to provide a brief explanation with the installation code for AutoML. Alternatively, I could use a try/except block to handle the import and installation if it’s missing, checking the version afterwards. Then, I’ll mention using the breast cancer dataset for fitting.

A good first step is to set up MLJAR AutoML in this notebook.
Your current dataset looks ready for it, and I don’t see any obvious ID-like leakage columns in X, so the 30 feature columns are fine to use.

</>Python

import sys
import subprocess

try:
    from supervised import AutoML
except ImportError:
    subprocess.check_call([sys.executable, "-m", "ensurepip", "--upgrade"])
    subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "mljar-supervised"])
    from supervised import AutoML

AutoML

Looking in links: /tmp/tmp5c40rdfq
Processing /tmp/tmp5c40rdfq/pip-26.0.1-py3-none-any.whl
Installing collected packages: pip
Successfully installed pip-26.0.1

try again

</>Install supervised AutoML

import sys
import subprocess
import importlib

try:
    from supervised import AutoML
except ModuleNotFoundError:
    subprocess.check_call([sys.executable, "-m", "ensurepip", "--upgrade"])
    subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "mljar-supervised"])
    importlib.invalidate_caches()
    from supervised import AutoML

AutoML

supervised.automl.AutoML

Ok so do AutoML now

Thinking

Planning evaluation process

I’m thinking about using the existing X_test later for evaluation. It might be useful to include predictions too. My main objective is to focus on training, and it seems like I’ll want to print a structured report as part of that process. Using an existing split makes sense. I’ll need to craft a concise explanation and the relevant code, but I can probably keep it to a single code block. Let's get started!

Let’s train MLJAR AutoML on the existing training split. This dataset has no obvious ID-like columns, so using X_train as-is is fine.

</>Train AutoML model and report

from supervised import AutoML

automl = AutoML(
    mode="Explain",
    eval_metric="logloss",
    total_time_limit=300,
    random_state=42
)

automl.fit(X_train, y_train)

report = automl.report_structured()
print(report)

AutoML directory: AutoML_1
The task is binary_classification with evaluation metric logloss
AutoML will use algorithms: ['Baseline', 'Linear', 'Decision Tree', 'Random Forest', 'Xgboost', 'Neural Network']
AutoML is generating and selecting models automatically. Review outputs before important use.
AutoML will ensemble available models
AutoML steps: ['simple_algorithms', 'default_algorithms', 'ensemble']
* Step simple_algorithms will try to check up to 3 models
1_Baseline logloss 0.662722 trained in 0.43 seconds
2_DecisionTree logloss 0.811861 trained in 7.93 seconds
3_Linear logloss 0.1084 trained in 6.57 seconds
* Step default_algorithms will try to check up to 3 models
4_Default_Xgboost logloss 0.093616 trained in 7.37 seconds
5_Default_NeuralNetwork logloss 0.142918 trained in 3.22 seconds
6_Default_RandomForest logloss 0.142346 trained in 4.38 seconds
* Step ensemble will try to check up to 1 model
Ensemble logloss 0.089164 trained in 3.23 seconds

2026-06-15 13:31:47,354 supervised.utils.automl_plots ERROR Failed to generate heatmap 'Top-25 (30 total) Feature Importance (Original Scale)': module 'matplotlib.cm' has no attribute 'get_cmap'
2026-06-15 13:31:47,356 supervised.utils.automl_plots ERROR Traceback (most recent call last):
  File "/home/neuralnine/Documents/Programming/NeuralNine/youtube-preparation/MLJARTutorial/.venv/lib/python3.13/site-packages/supervised/utils/automl_plots.py", line 47, in _plot_feature_heatmap
    cmap=plt.cm.get_cmap(cmap),
         ^^^^^^^^^^^^^^^
AttributeError: module 'matplotlib.cm' has no attribute 'get_cmap'

2026-06-15 13:31:47,367 supervised.utils.automl_plots ERROR Failed to generate heatmap 'Top-25 (30 total) Scaled Feature Importance (MinMax per model)': module 'matplotlib.cm' has no attribute 'get_cmap'
2026-06-15 13:31:47,368 supervised.utils.automl_plots ERROR Traceback (most recent call last):
  File "/home/neuralnine/Documents/Programming/NeuralNine/youtube-preparation/MLJARTutorial/.venv/lib/python3.13/site-packages/supervised/utils/automl_plots.py", line 47, in _plot_feature_heatmap
    cmap=plt.cm.get_cmap(cmap),
         ^^^^^^^^^^^^^^^
AttributeError: module 'matplotlib.cm' has no attribute 'get_cmap'

2026-06-15 13:31:47,388 supervised.utils.automl_plots ERROR An error occurred during model correlation plotting: module 'matplotlib.cm' has no attribute 'get_cmap'
2026-06-15 13:31:47,389 supervised.utils.automl_plots ERROR Traceback (most recent call last):
  File "/home/neuralnine/Documents/Programming/NeuralNine/youtube-preparation/MLJARTutorial/.venv/lib/python3.13/site-packages/supervised/utils/automl_plots.py", line 336, in models_correlation
    cmap=plt.cm.get_cmap("Blues"),
         ^^^^^^^^^^^^^^^
AttributeError: module 'matplotlib.cm' has no attribute 'get_cmap'

AutoML fit time: 47.97 seconds
AutoML best model: Ensemble
# MLJAR AutoML Report

> Models in this report were generated and selected automatically by MLJAR AutoML. Review model behavior, data suitability, and decision impact before important use.

## Leaderboard

| name                    | model_type     | metric_type   |   metric_value |   train_time |
|:------------------------|:---------------|:--------------|---------------:|-------------:|
| 1_Baseline              | Baseline       | logloss       |      0.662722  |         1.66 |
| 2_DecisionTree          | Decision Tree  | logloss       |      0.811861  |         9.45 |
| 3_Linear                | Linear         | logloss       |      0.1084    |         8.43 |
| 4_Default_Xgboost       | Xgboost        | logloss       |      0.0936158 |         9.36 |
| 5_Default_NeuralNetwork | Neural Network | logloss       |      0.142918  |         4.94 |
| 6_Default_RandomForest  | Random Forest  | logloss       |      0.142346  |         6.01 |
| Ensemble                | Ensemble       | logloss       |      0.0891638 |         3.23 |

## Global Feature Importance (Averaged Across Models)

Method: `mean_rank_across_models`

Models used: `5`, Features: `30`

### Most Influential Features (Top 10 by mean rank)

| Feature              |   Mean Rank |
|:---------------------|------------:|
| worst perimeter      |         2.6 |
| worst texture        |         4   |
| worst radius         |         4.2 |
| area error           |         4.2 |
| mean concave points  |         7.4 |
| worst area           |         8.3 |
| worst concave points |         9.9 |
| worst concavity      |        11.3 |
| mean texture         |        11.5 |
| perimeter error      |        13.7 |

### Least Influential Features (Bottom 10 by mean rank)

| Feature                 |   Mean Rank |
|:------------------------|------------:|
| mean radius             |        20.1 |
| mean symmetry           |        20.5 |
| mean fractal dimension  |        20.9 |
| concavity error         |        21.1 |
| worst fractal dimension |        21.9 |
| smoothness error        |        22.3 |
| concave points error    |        22.9 |
| compactness error       |        23.7 |
| fractal dimension error |        24.6 |
| symmetry error          |        25.2 |

AutoML did run successfully, and it found a better model family than the earlier Random Forest baseline.

From the observed training output:

MLJAR detected this as a binary classification problem and optimized logloss.
It tried these model families:
Baseline
Linear
Decision Tree
Random Forest
Xgboost
Neural Network
The strongest single model shown was Default_Xgboost with logloss around 0.0936.
The overall best model was the Ensemble, which means MLJAR combined multiple models and judged that combination best on validation.

What this means relative to your earlier work:

Your earlier hand-built Random Forest was already solid, but AutoML found that XGBoost and the ensemble performed better by the chosen metric.
The Linear model was also surprisingly strong on this dataset, which is common for breast cancer data because the classes are fairly separable.
The Decision Tree performed much worse than the others, suggesting a single tree is too unstable/simple here.

One caution from the output:

There were plot-generation errors related to matplotlib.cm.get_cmap.
That did not stop training. The models trained and the report was still produced.
So this is mainly a reporting/visualization compatibility issue, not a modeling failure.

Bottom line:

AutoML succeeded.
Best result: Ensemble
Best single model visible: Default_Xgboost
AutoML suggests that a boosted/ensemble approach is better than the earlier plain Random Forest for this dataset.

Show all people that own stuff

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Still connected to the db?

Thinking

Considering database connection check

I’m thinking about how to check the database connection. I could run a simple query like SELECT 1, but the instructions say to return a single fenced SQL block only when a database query is needed. Since the user is just asking if I'm still connected, I don’t want to include code unless they specifically ask for it. It might be better to mention that the notebook state isn’t relevant and to keep my response concise.

I still have the database context in this chat, but I can’t directly confirm the live PostgreSQL connection state from here.

If you want, I can check it with a tiny query next.

Yes

Thinking

Crafting concise SQL

I want to focus on creating a single, concise SQL block for the task at hand. My goal is to make it clear and effective while ensuring it meets the necessary requirements. By narrowing it down to just one step, I can streamline the process and avoid any unnecessary complexity. It’s all about being efficient and precise, which will ultimately help me better serve the user's needs!

Let’s verify the connection with a minimal PostgreSQL check:

SQLShow database info

SELECT
    current_database() AS database_name,
    current_user AS user_name,
    now() AS server_time;