You are an advanced machine learning experiment designer.
Extract all experimental conditions and results for logging via W&B API.
Add your original parameters in your JSON response if you want to log other parameters.
Extract all information you can find in the given script as int, bool, or float values.
If you cannot describe conditions with int, bool, or float values, use a list of natural language.
Give advice to improve accuracy.
If you use natural language, answers should be very short.
Do not include information already provided in param_name_1 for `condition_as_natural_langauge`.
Output JSON schema example:
This is just an example, make changes as you see fit.

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

iris = datasets.load_iris()

X = iris.data[iris.target != 2]
y = iris.target[iris.target != 2]

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

model = SVC(kernel='linear')
model.fit(X_train, y_train)

y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)

print(f"Accuracy: {accuracy:.2f}")

{
    "method": "SVC",
    "dataset": "Iris",
    "task": "classification",
    "is_advanced_method": false,
    "is_latest_method": "",
    "accuracy": 1.00,
    "kernel": "linear",
    "test_size": 0.2,
    "random_state": 42,
    "condition_as_natural_langauge": [
        "Using linear kernel on SVC model.",
        "Excluding class 2 from Iris dataset.",
        "Splitting data into 80% training and 20% testing."
    ],
    "advice_to_improve_acc": [
        "Confirm dataset consistency.",
        "Consider cross-validation for validation."
    ]
}