SAS
*SAS offers a number of tools for automating machine learning tasks, such as data preparation, model selection, and hyperparameter tuning. These tools are part of the SAS Visual Data Mining and Machine Learning (VDMML) platform, which is a suite of data mining and machine learning algorithms and visualizations.
One of the key features of SAS VDMML is the AUTOML procedure, which provides an automated way to build and evaluate machine learning models. The AUTOML procedure can be used to build models for a variety of tasks, including classification, regression, clustering, and time series forecasting.
To use the AUTOML procedure, you first need to define the input data and the target variable that you want to predict. You can then specify the type of model you want to build and the evaluation metric you want to use, and the AUTOML procedure will automatically build and evaluate a number of different models to find the best one.
Here's an example of how to use the AUTOML procedure in SAS:;
proc automl data=input_dataset
target=target_variable
metric=roc;
run;
*This code will build and evaluate a number of different machine learning models for the task of classification, using the ROC curve as the evaluation metric. The AUTOML procedure will select the best model based on the evaluation metric and return the results in the SAS log and output window.;
One of the key features of SAS VDMML is the AUTOML procedure, which provides an automated way to build and evaluate machine learning models. The AUTOML procedure can be used to build models for a variety of tasks, including classification, regression, clustering, and time series forecasting.
To use the AUTOML procedure, you first need to define the input data and the target variable that you want to predict. You can then specify the type of model you want to build and the evaluation metric you want to use, and the AUTOML procedure will automatically build and evaluate a number of different models to find the best one.
Here's an example of how to use the AUTOML procedure in SAS:;
proc automl data=input_dataset
target=target_variable
metric=roc;
run;
*This code will build and evaluate a number of different machine learning models for the task of classification, using the ROC curve as the evaluation metric. The AUTOML procedure will select the best model based on the evaluation metric and return the results in the SAS log and output window.;
Python
"""
There are several Python libraries that provide automated machine learning (AutoML) capabilities, such as autosklearn, TPOT, and H2OAutoML.
Here is an example of how to use the autosklearn library to perform AutoML in Python:"""
import autosklearn.classification
import pandas as pd
# Load the input data and target variable
X = pd.read_csv("input_data.csv")
y = pd.read_csv("target_variable.csv", header=None)
# Create the AutoML object
automl = autosklearn.classification.AutoSklearnClassifier()
# Fit the model to the data
automl.fit(X, y)
# Make predictions
predictions = automl.predict(X)
"""This code will build and evaluate a number of different machine learning models for the task of classification, using default evaluation metrics. The AutoSklearnClassifier object will select the best model based on the evaluation metric and return the predictions for the input data.
You can also use the TPOT library to perform AutoML in Python. Here is an example of how to use TPOT:"""
import tpot
import pandas as pd
# Load the input data and target variable
X = pd.read_csv("input_data.csv")
y = pd.read_csv("target_variable.csv", header=None)
# Create the TPOT object
tpot = tpot.TPOTClassifier()
# Fit the model to the data
tpot.fit(X, y)
# Make predictions
predictions = tpot.predict(X)
"""This code will build and evaluate a number of different machine learning models for the task of classification, using default evaluation metrics. The TPOTClassifier object will select the best model based on the evaluation metric and return the predictions for the input data.
In the examples I provided, X represents the input data and y represents the target variable. X should be a two-dimensional array-like object, such as a NumPy array or a pandas DataFrame, where each row corresponds to a single sample and each column corresponds to a feature. y should be a one-dimensional array-like object, such as a NumPy array or a pandas Series, where each element corresponds to the target variable for a single sample.
Here is an example of how X and y might be structured for a classification task:"""
import numpy as np
# Input data (4 samples, 3 features)
X = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
[10, 11, 12]])
# Target variable (4 samples)
y = np.array([0, 1, 0, 1])
"""In this example, X has 4 rows (samples) and 3 columns (features), and y has 4 elements (samples). Each row in X corresponds to a single sample, and each element in y corresponds to the target variable for that sample.
While the last script used an numpy array, using a dataframe also works with autosklearn or TPOT libraries."""
There are several Python libraries that provide automated machine learning (AutoML) capabilities, such as autosklearn, TPOT, and H2OAutoML.
Here is an example of how to use the autosklearn library to perform AutoML in Python:"""
import autosklearn.classification
import pandas as pd
# Load the input data and target variable
X = pd.read_csv("input_data.csv")
y = pd.read_csv("target_variable.csv", header=None)
# Create the AutoML object
automl = autosklearn.classification.AutoSklearnClassifier()
# Fit the model to the data
automl.fit(X, y)
# Make predictions
predictions = automl.predict(X)
"""This code will build and evaluate a number of different machine learning models for the task of classification, using default evaluation metrics. The AutoSklearnClassifier object will select the best model based on the evaluation metric and return the predictions for the input data.
You can also use the TPOT library to perform AutoML in Python. Here is an example of how to use TPOT:"""
import tpot
import pandas as pd
# Load the input data and target variable
X = pd.read_csv("input_data.csv")
y = pd.read_csv("target_variable.csv", header=None)
# Create the TPOT object
tpot = tpot.TPOTClassifier()
# Fit the model to the data
tpot.fit(X, y)
# Make predictions
predictions = tpot.predict(X)
"""This code will build and evaluate a number of different machine learning models for the task of classification, using default evaluation metrics. The TPOTClassifier object will select the best model based on the evaluation metric and return the predictions for the input data.
In the examples I provided, X represents the input data and y represents the target variable. X should be a two-dimensional array-like object, such as a NumPy array or a pandas DataFrame, where each row corresponds to a single sample and each column corresponds to a feature. y should be a one-dimensional array-like object, such as a NumPy array or a pandas Series, where each element corresponds to the target variable for a single sample.
Here is an example of how X and y might be structured for a classification task:"""
import numpy as np
# Input data (4 samples, 3 features)
X = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
[10, 11, 12]])
# Target variable (4 samples)
y = np.array([0, 1, 0, 1])
"""In this example, X has 4 rows (samples) and 3 columns (features), and y has 4 elements (samples). Each row in X corresponds to a single sample, and each element in y corresponds to the target variable for that sample.
While the last script used an numpy array, using a dataframe also works with autosklearn or TPOT libraries."""