The article showcases concise Python code snippets (one-liners) for common machine learning tasks like data splitting, standardization, model training (linear regression, logistic regression, decision tree, random forest), and prediction, leveraging libraries such as scikit-learn.
| **#** | **One-Liner** | **Description** | **Library** | **Use Case** |
|-----|-----------------------------------------------------|-------------------------------------------------------------------------------------|-------------------|-------------------------------------------------|
| 1 | `from sklearn.datasets import load_iris; X, y = load_iris(return_X_y=True)` | Loads the Iris dataset, a classic for classification. | scikit-learn | Loading a standard dataset. |
| 2 | `from sklearn.model_selection import train_test_split; X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)` | Splits the dataset into training and testing sets. | scikit-learn | Preparing data for model training & evaluation.|
| 3 | `from sklearn.linear_model import LogisticRegression; model = LogisticRegression(random_state=1)` | Creates a Logistic Regression model. | scikit-learn | Binary Classification. |
| 4 | `model.fit(X_train, y_train)` | Trains the Logistic Regression model. | scikit-learn | Model training. |
| 5 | `y_pred = model.predict(X_test)` | Predicts labels for the test dataset. | scikit-learn | Making predictions. |
| 6 | `from sklearn.metrics import accuracy_score; accuracy = accuracy_score(y_test, y_pred)` | Calculates the accuracy of the model. | scikit-learn | Evaluating model performance. |
| 7 | `import pandas as pd; df = pd.DataFrame(X, columns=iris.feature_names)` | Creates a Pandas DataFrame from the Iris dataset features. | Pandas | Data manipulation and analysis. |
| 8 | `df 'target' » = y` | Adds the target variable to the DataFrame. | Pandas | Combining features and labels. |
| 9 | `df.head()` | Displays the first few rows of the DataFrame. | Pandas | Inspecting the data. |
| 10 | `df.describe()` | Generates descriptive statistics of the DataFrame. | Pandas | Understanding data distribution. |
| **#** | **One-Liner** | **Description** | **Library** | **Use Case** |
|-----|-----------------------------------------------------|-------------------------------------------------------------------------------------|-------------------|-------------------------------------------------|
| 1 | `from sklearn.datasets import load_iris; X, y = load_iris(return_X_y=True)` | Loads the Iris dataset, a classic for classification. | scikit-learn | Loading a standard dataset. |
| 2 | `from sklearn.model_selection import train_test_split; X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)` | Splits the dataset into training and testing sets. | scikit-learn | Preparing data for model training & evaluation.|
| 3 | `from sklearn.linear_model import LogisticRegression; model = LogisticRegression(random_state=1)` | Creates a Logistic Regression model. | scikit-learn | Binary Classification. |
| 4 | `model.fit(X_train, y_train)` | Trains the Logistic Regression model. | scikit-learn | Model training. |
| 5 | `y_pred = model.predict(X_test)` | Predicts labels for the test dataset. | scikit-learn | Making predictions. |
| 6 | `from sklearn.metrics import accuracy_score; accuracy = accuracy_score(y_test, y_pred)` | Calculates the accuracy of the model. | scikit-learn | Evaluating model performance. |
| 7 | `import pandas as pd; df = pd.DataFrame(X, columns=iris.feature_names)` | Creates a Pandas DataFrame from the Iris dataset features. | Pandas | Data manipulation and analysis. |
| 8 | `df 'target' » = y` | Adds the target variable to the DataFrame. | Pandas | Combining features and labels. |
| 9 | `df.head()` | Displays the first few rows of the DataFrame. | Pandas | Inspecting the data. |
| 10 | `df.describe()` | Generates descriptive statistics of the DataFrame. | Pandas | Understanding data distribution. |
Additive Decision Trees are a variation of standard decision trees, constructed in a way that can often allow them to be more accurate, more interpretable, or both. This article explains the intuition behind Additive Decision Trees and how they can be constructed.
