Blended learning can be done in parallel or in combination. In the parallel method, several algorithms work on the data simultaneously and their results are combined to reach a final answer. In the combined method, different algorithms are executed in order and the output of each one is used as input for the next algorithm. The advantages of hybrid learning include increasing the accuracy and performance of algorithms, improving flexibility, the ability to compensate for algorithm errors, and increasing the independence of algorithms from each other. This method is especially useful in complex problems and big data, and can provide significant improvement in the performance of machine learning algorithms.
How does blended learning work?
Blended learning is generally implemented in two main ways, which are called ensemble and sequential training. Each method has different approaches and algorithms, which I will introduce below.
1. Ensemble
Aggregation or more precisely, ensemble in machine learning is a method that uses the combination of several machine learning models to improve the efficiency and accuracy of prediction. In this method, several independent models are created and their results are combined to reach a final answer. Each model may use a similar or different machine learning algorithm or be trained using different settings and parameters.
One of the famous methods of the collective model is the Majority Voting method. In this method, several machine learning models are trained with the same algorithm or different algorithms. When forecasting, each model provides its own answer and by applying a certain rule (e.g. majority vote), the final answer is determined. For example, in the binary classification problem, if the largest number of models belong to one class, the final prediction also belongs to that class. There are other ways to aggregate. Bootstrap Aggregating is one of them. In this method, a number of random samples of data are created and each sample is assigned to a machine learning model. Then, the responses of these models are combined and their average or majority is used as the final response. Next up is the Boosting mix. In this method, a series of machine learning models are trained sequentially. Each model tries to focus on the instances where the previous models had errors in order to improve the errors. The results of these models are combined using methods such as bootstrap aggregation or weighting. The ensemble approach in machine learning provides us with good advantages, some of which are as follows:
Improving prediction accuracy: By combining the results of several machine learning models, the prediction accuracy can be increased, as each model may focus on a part of the data or certain aspects of the problem and show its strengths. By combining the results, the strengths of each model can be extracted and the overall accuracy can be improved.
Stability against noise and deviations: Aggregation can increase the stability against noise and deviations of the data. If one model performs poorly due to noise or skew in the data, other models can compensate for this problem and determine the correct answer.
Better generalizability: Using the collective approach, the generalizability of models can be improved. Different models are trained with different algorithms and settings and look at the data from different perspectives. The above approach makes the models have a better ability to generalize to new and unknown data.
Reducing the connection between models: By using independent models in aggregation, the connection between models is reduced. More precisely, if a particular model performs poorly due to problems such as incorrect data or training errors, other models will be able to correct these errors and provide the correct answer. In general, aggregation is a powerful method in machine learning that can increase the accuracy and efficiency of prediction and make models more resistant to noise and deviations.
2. Sequential Training
Sequential training in machine learning refers to a method in which the machine learning model is taught sequentially and in several stages. In this method, the model is gradually trained in successive batches of data and improves its parameters. Sequential training process is usually used in problems where the data are sequential and temporally related. Among these issues, the following should be mentioned:
Forecasting time series: In this problem, the goal is to predict future values of time series. The machine learning model here is trained using past data and then gradually makes future predictions.
Machine translation: In this case, the goal is to translate text from one language to another. The machine learning model learns translation rules by using sentences and educational texts, gradually and sequentially and according to the structure of sentences and words.
Detecting objects in videos: In this problem, the goal is to detect and determine the position of objects in videos. Here, the machine learning model gradually learns rules and patterns related to objects by analyzing the sequence of video frames.
In sequential training, the machine learning model is first trained using the initial data. Then, gradually and at each step, the model is trained with new batches of data and updates its parameters. In other words, the model gradually collects more information about the data and rules related to the problem in each step.
The advantage of sequential training in machine learning is that the model is able to acquire information related to new data at each step and its performance increases over time. Also, this method can largely solve the problems that usually occur in batch training, such as overfitting. However, sequential training requires more time and resources because we need to train the model in several steps and repeat each step with new data. Overall, sequential training is a useful method that can help improve the performance and accuracy of machine learning models in problems where the data is sequential. In both aggregation and sequential training methods, the ultimate goal of combining multiple machine learning algorithms is to improve the accuracy and performance of the model prediction. By combining the results of several algorithms, it is possible to extract the strengths of each algorithm and compensate for the weaknesses. In addition, hybrid learning can increase the resistance to noise and data deviations and improve the generalizability of models.
How is blended learning implemented?
As we mentioned, combined learning is a method that improves performance and final accuracy by combining and aggregating the predictions of several machine learning models. This method is usually used in problems where the data is complex or different models do not perform best for various reasons. Implementation of blended learning can be done in several ways. Below, I describe two common ways to implement blended learning:
Voting: In this method, a number of independent machine learning models are trained and then their predictions are combined. Voting can be done by majority voting or weighted voting. In majority voting, the overall prediction is made based on the majority of the results of all models. In weighted voting, a weight is assigned to each model and the overall prediction is determined according to the weights of the models.
Bootstrap Aggregating: In this method, several random samples of training data are created and a machine learning model is trained on each sample. The predictions from each model are then combined to produce a final prediction. This method is usually done using the majority voting method.
In both voting and bootstrapping methods, the main advantage of hybrid learning is in improving the independence of models and reducing overfitting. By combining the predictions of several models, we can exploit the diversity and aggregation power of each model and improve the final performance. To implement blended learning, you can use the libraries available in your programming language. Also, in some famous machine learning libraries such as scikit-learn in Python, the implementation of blended learning methods such as voting and bootstrapping can be done in an easier way. For example, you can use the VotingClassifier class for voting and the BaggingClassifier class for bootstrapping. These libraries do all the details of implementing hybrid learning methods for you, and you only need to define and set up the models you want.
In the first step, you need to train and tune different models. Then send the models as input for voting or bootstrapping using the corresponding classes in the library. Finally, the trained hybrid learning model can be used to make predictions on test data. For example, in scikit-learn, to use voting you can do the following:
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
# Definition of different models
model1 = LogisticRegression()
model2 = DecisionTreeClassifier()
model3 = SVC()
# Definition of voting
voting_model = VotingClassifier(estimators=[('lr', model1), ('dt', model2), ('svm', model3)], voting='hard')
# Training models
voting_model.fit(X_train, y_train)
# Forecast
predictions = voting_model.predict(X_test)
In this example, logistic regression, decision tree and support vector machine models have been used as different models for voting. Then, using the fit function, the models are trained and using the predict function, the final predictions are made on the test data. Depending on the need and problem, you can implement other strategies for blended learning.
How to implement blended learning in Python
In the following code snippet, we use the scikit-learn library to implement blended learning in Python. This library has suitable classes for polling and bootstrapping. Below is a sample code to implement voting and bootstrapping in Python using scikit-learn:
1. Voting:
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# Load training and testing data
data = load_iris()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)
# Definition of different models
model1 = LogisticRegression()
model2 = DecisionTreeClassifier()
model3 = SVC()
# Definition of voting
voting_model = VotingClassifier(estimators=[('lr', model1), ('dt', model2), ('svm', model3)], voting='hard')
# Training models
voting_model.fit(X_train, y_train)
# Forecast
predictions = voting_model.predict(X_test)
# Calculate accuracy
accuracy = accuracy_score(y_test, predictions)
print("Accuracy:", accuracy)
In this example, the Iris dataset is used. Then logistic regression models, decision tree and support vector machine are defined as different models for voting. Then, using the fit function, the models are trained and using the predict function, the final predictions are made on the test data. Finally, the accuracy of the predictions is calculated and printed using the accuracy_score function.
2. Bootstrapping (Bagging):
from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# Load training and testing data
data = load_iris()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)
# Define the main model
base_model = DecisionTreeClassifier()
# Define bootstrapping
bagging_model = BaggingClassifier(base_estimator=base_model, n_estimators=10)
# Model training
bagging_model.fit(X_train, y_train)
# Forecast
predictions = bagging_model.predict(X_test)
# Calculate accuracy
accuracy = accuracy_score(y_test, predictions)
print("Accuracy:", accuracy)
In this example, the Iris dataset is used. A decision tree model is defined as the main model. Then, using the BaggingClassifier command and setting base_estimator to the main model and n_estimators to the number of bootstrapping models, a bootstrapping model is created. Then, using the fit function, the model is trained and using the predict function, the final predictions are made on the test data. Finally, the accuracy of the predictions is calculated and printed using the accuracy_score function. If you want to use compound regression, you can use VotingRegressor class instead of VotingClassifier and add the regression models as different models. Using these examples, you can implement hybrid learning in Python and adapt it to your needs by changing parameters and model types.
Let's extend the above code snippet to make it work better. We can use different techniques such as feature selection, sample boosting or parameter tuning. Now we expand the above code snippet as below.
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score
# Load training and testing data
data = load_iris()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)
# Convert attributes
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# Selection of important features
selector = SelectKBest(score_func=f_classif, k=2)
X_train_selected = selector.fit_transform(X_train_scaled, y_train)
X_test_selected = selector.transform(X_test_scaled)
# Definition of different models
model1 = LogisticRegression()
model2 = DecisionTreeClassifier()
model3 = SVC()
# Definition of voting
voting_model = VotingClassifier(estimators=[('lr', model1), ('dt', model2), ('svm', model3)], voting='hard')
# Pipeline implementation
pipeline = Pipeline([
('scaler', scaler),
('selector', selector),
('voting', voting_model)
])
# Set voting parameters
param_grid = {
'voting__lr__C': [0.1, 1, 10],
'voting__dt__max_depth': [None, 5, 10],
'voting__svm__C': [0.1, 1, 10],
'voting__svm__gamma': [0.1, 1, 10]
}
# Choosing the best parameters using GridSearchCV
grid_search = GridSearchCV(pipeline, param_grid=param_grid, cv=5)
grid_search.fit(X_train, y_train)
# Forecast
predictions = grid_search.predict(X_test)
# Calculate accuracy
accuracy = accuracy_score(y_test, predictions)
print("Accuracy:", accuracy)
print("Best Parameters:", grid_search.best_params_)
In this example, more steps are added to implement blended learning. The first is feature transformation, which uses StandardScaler to transform features using scaling. Next is the selection of important features, which uses SelectKBest to select k important features based on statistics. The next thing is the implementation of the pipeline (Pipeline), using the Pipeline, the preprocessing steps and the voting model are placed in a sequential flow. Next, parameters are adjusted, using GridSearchCV, better parameters are selected for the voting model.
This example runs on the Iris dataset. You can replace the datasets and models with your own input data and models. You can also add other techniques such as oversampling/undersampling or using kernel functions to the code, depending on your specific problem.
