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. |
History-based Feature Selection (HBFS) is a feature selection tool that aims to identify an optimal subset of features for prediction problems. It is designed to work similarly to wrapper methods and genetic methods, focusing on selecting feature subsets that yield the highest performance for a given dataset and target. HBFS differs from filter methods, which evaluate and rank individual features based on their predictive power. Instead, HBFS evaluates combinations of features over multiple iterations, using a Random Forest regressor to estimate performance and iteratively refining feature sets. This tool supports binary and multiclass classification, as well as regression, and allows for balancing the trade-off between maximizing accuracy and minimizing the number of features through parameters such as maximum features and penalties. Examples provided demonstrate the use of HBFS with various models and metrics, showcasing its ability to improve model performance by identifying optimal feature subsets.
The article discusses the credibility of using Random Forest Variable Importance for identifying causal links in data where the output is binary. It contrasts this method with fitting a Logistic Regression model and examining its coefficients. The discussion highlights the challenges of extracting causality from observational data without controlled experiments, emphasizing the importance of domain knowledge and the use of partial dependence plots for interpreting model results.
- Extreme Gradient Boosting: A quick and reliable regressor and classifier
- Summary: LightGBM is faster and better though XGBoost is close
- Summary: LightGBM is faster and better though XGBoost is close
In this post, I’ll discuss random forests, another popular approach for feature ranking.
Random forest feature importance
Random forests are among the most popular machine learning methods thanks to their relatively good accuracy, robustness and ease of use. They also provide two straightforward methods for feature selection: mean decrease impurity and mean decrease accuracy.
Random forest feature importance
Random forests are among the most popular machine learning methods thanks to their relatively good accuracy, robustness and ease of use. They also provide two straightforward methods for feature selection: mean decrease impurity and mean decrease accuracy.
