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. |
This example demonstrates Density-Based Spatial Clustering of Applications with Noise (DBSCAN) using scikit-learn, showing how to generate synthetic clusters, compute DBSCAN clustering, and visualize the results, including core and non-core samples.
emlearn is an open-source machine learning inference engine designed for microcontrollers and embedded devices. It supports various machine learning models for classification, regression, unsupervised learning, and feature extraction. The engine is portable, with a single header file include, and uses C99 code and static memory allocation. Users can train models in Python and convert them to C code for inference.
Scikit-learn — the go-to library for machine learning offering a user friendly, consistent interface.
Pycaret — lowering the entry point for machine learning with low code, automated and end to end solutions.
PyTorch — build and deploy powerful, scalable neural networks with its highly flexible architecture.
TensorFlow — one of the most mature deep learning libraries, highly flexible and suited to a wide range of applications.
Keras — TensorFlow made simple.
FastAI — makes deep learning more accessible with a high-level API built on top of PyTorch.
Pycaret — lowering the entry point for machine learning with low code, automated and end to end solutions.
PyTorch — build and deploy powerful, scalable neural networks with its highly flexible architecture.
TensorFlow — one of the most mature deep learning libraries, highly flexible and suited to a wide range of applications.
Keras — TensorFlow made simple.
FastAI — makes deep learning more accessible with a high-level API built on top of PyTorch.
Comparing Clustering Algorithms
Following table will give a comparison (based on parameters, scalability and metric) of the clustering algorithms in scikit-learn.
Sr.No Algorithm Name Parameters Scalability Metric Used
1 K-Means No. of clusters Very large n_samples The distance between points.
2 Affinity Propagation Damping It’s not scalable with n_samples Graph Distance
3 Mean-Shift Bandwidth It’s not scalable with n_samples. The distance between points.
4 Spectral Clustering No.of clusters Medium level of scalability with n_samples. Small level of scalability with n_clusters. Graph Distance
5 Hierarchical Clustering Distance threshold or No.of clusters Large n_samples Large n_clusters The distance between points.
6 DBSCAN Size of neighborhood Very large n_samples and medium n_clusters. Nearest point distance
7 OPTICS Minimum cluster membership Very large n_samples and large n_clusters. The distance between points.
8 BIRCH Threshold, Branching factor Large n_samples Large n_clusters The Euclidean distance between points.
Following table will give a comparison (based on parameters, scalability and metric) of the clustering algorithms in scikit-learn.
Sr.No Algorithm Name Parameters Scalability Metric Used
1 K-Means No. of clusters Very large n_samples The distance between points.
2 Affinity Propagation Damping It’s not scalable with n_samples Graph Distance
3 Mean-Shift Bandwidth It’s not scalable with n_samples. The distance between points.
4 Spectral Clustering No.of clusters Medium level of scalability with n_samples. Small level of scalability with n_clusters. Graph Distance
5 Hierarchical Clustering Distance threshold or No.of clusters Large n_samples Large n_clusters The distance between points.
6 DBSCAN Size of neighborhood Very large n_samples and medium n_clusters. Nearest point distance
7 OPTICS Minimum cluster membership Very large n_samples and large n_clusters. The distance between points.
8 BIRCH Threshold, Branching factor Large n_samples Large n_clusters The Euclidean distance between points.
Linear Regression
Multiple Regression
Polynomial Regression
Decision Tree
Logistic Regression
K Nearest Neighbor
Naive Bayes
Random Forest
Support Vector Machines
Principal Component Analysis
Linear Discriminant Analysis
K Means Clustering
Hierarchical Clustering
Multiple Regression
Polynomial Regression
Decision Tree
Logistic Regression
K Nearest Neighbor
Naive Bayes
Random Forest
Support Vector Machines
Principal Component Analysis
Linear Discriminant Analysis
K Means Clustering
Hierarchical Clustering
