Scikit-learn, also known as sklearn, is Python’s most usable and robust Machine Learning library. It allows you to use classification, regression, clustering, dimensionality reduction, and other helpful ML and statistical modeling algorithms without the need to code them yourself. This article will cover the sklearn and basic statistical modeling algorithms you can use by including this module in your Python programs.
Table of contents
- Installation of sklearn
- Data pre-processing in sklearn module
- Supervised Machine Learning algorithms
- Unsupervised Machine Learning algorithms
- ML model evaluation methods
- Models cross-validation
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Installation of sklearn
David Cournapeau initially developed Sklearn as a Google Summer of Code project in 2007. Later many other professionals contributed to this project and took it to another level. The scikit-learn project is hosted on GitHub, where anyone can contribute to it.
The most common and popular way of installing the sklearn module on your system is using the
#installing sklearn module pip install scikit-learn
Once you have installed the sklearn module, you can check its version from your Python code:
# importing the module import sklearn # printing the version print(sklearn.__version__)
You can update the existing installed sklearn module on your system by typing the following command:
pip install --upgrade scikit-learn
Data pre-processing in sklearn module
The data that we usually deal with is stored in so-called raw format, and we need to process it to make it suitable for ML algorithms. This process is called preprocessing, and it allows us to check missing values, noisy data, and other inconsistencies before training the ML model. This section will use the sklearn module to preprocess the data.
For example, let’s say that we have the following dataset:
# importing numpy import numpy as np # creating numpy dataset data = np.array([ [3.4, -1.8, 5.1], [-4.5, 2.4, 3.3], [3.5, -7.2, 5.1], [5.7, 2.5, -2.8]] ) # printing print(data)
Now, let’s apply different methods to preprocess this dataset.
Binarization of dataset
Binarization is the technique used to convert the numerical values into boolean values. This process will convert all negative values to zero (False) and positive values to 1 (True).
# importing the module from sklearn import preprocessing # converting to binary dataset binary_data = preprocessing.Binarizer().transform(data) # printing print(binary_data)
We can also specify the threshold value for the binary conversion. By default, the threshold value is 0: any values above the threshold value will be considered 1, and below ones will be considered 0.
# converting to binary dataset threshold value 3 binary_data = preprocessing.Binarizer(threshold=3).transform(data) # printing print(binary_data)
Standardize a dataset along any axis
To standardize a dataset along any axis, you need to use the preprocessing.scale() method of the sklearn module. This method allows you to eliminate the mean from the feature vector so that every feature is centered on zero, and the standard deviation will be 1.
Do not usescikit-learn.org
scaleunless you know what you are doing. A common mistake is to apply it to the entire data before splitting into training and test sets. This will bias the model evaluation because information would have leaked from the test set to the training set.
# Mean and the standard deviation of the dataset print("Mean =", data.mean(axis=0)) print("Std-deviation =", data.std(axis=0))
Let’s scale our data so that every feature is centered at zero and the standard deviation becomes 1.
# scaling dataset data_scaled = preprocessing.scale(data) # printing mean and deviation print("Mean_removed =", data_scaled.mean(axis=0)) print("Stddeviation_removed =", data_scaled.std(axis=0))
After scaling, the data looks like this:
# printing scaled data print(data_scaled)
Scaling the dataset
The preprocessing.MinMaxScaler class is used to scale the feature vectors. The scaling of feature vectors is essential to reduce features influence on the ML model output. We can specify the range for scaling as shown below:
# specifiying range for scalling data_scaled = preprocessing.MinMaxScaler(feature_range=(0,1)) # scalling the dataset data_scaled = data_scaled.fit_transform(data) # printing scaled dataset print ("scaled data is :\n", data_scaled)
Note that the data has been scaled between 0 and 1. Similarly, we can also specify other ranges, and the elements will be arranged accordingly.
In preprocessing, normalization is used to modify the feature vectors. Sometimes, it is required to measure the feature vectors using a common scale. The sklearn module provides two different types of normalization techniques.
l1 normalization or Least Absolute Deviations technique modifies the value in such a way that the sum of the absolute values always remains up to 1 in each row, for example:
# normalizing the data using Least Absolute Deviations l1_normalization = preprocessing.normalize(data, norm='l1') # printing print("L1 normalized data is :\n", l1_normalization)
We can verify that the sum of absolute values in each of the rows is 1:
# finding sum Sum=[sum(abs(i)) for i in l1_normalization] # printing print (Sum)
l2 normalization or Least Squares modifies the value so that the sum of the squares always remains up to 1 in each row. Let us apply this method of normalization to our data:
# l2 normalization technique l2_normalization = preprocessing.normalize(data, norm='l2') # printing print("L1 normalized data is :\n", l2_normalization)
We can verify that the sum of squares in each row is equal to 1.
# finding sum of squares Sum=[sum(i*i) for i in l2_normalization] # printing print (Sum)
Supervised Machine Learning algorithms
There are three different types of machine learning algorithms: supervised machine learning, unsupervised machine learning, and reinforcement learning. The sklearn module is one of the most popular modules for Machine Learning as it provides various kinds of built-in ML algorithms, which we will discuss in this section.
Before applying these algorithms, let’s load the dataset and split it into the training and testing part. We will use the Iris dataset (one of the most popular ML datasets for education).
We can import the Iris dataset from the datasets module of sklearn.
# importing the dataset from sklearn.datasets import load_iris # loading dataset iris = load_iris()
Let’s divide the dataset into features and target variables (outputs) and print them out:
# input and target variables Inputs = iris.data Target= iris.target # getiing features names and target variables feature_names = iris.feature_names target_names = iris.target_names # printing print("Feature names:", feature_names) print("Target names:", target_names)
The next essential feature available in the sklearn module allows us to split datasets into the training and testing parts. The training part is used to train the model, and the testing part is used to test/validate the model’s predictions.
# importing the module from sklearn.model_selection import train_test_split # splitting the dataset into testing and training part X_train, X_test, y_train, y_test = train_test_split( Inputs, Target, test_size = 0.25)
train_test_split method will randomly assign 25% of the data to testing and 75% to the training part.
Support Vector Machine (SVM) algorithm
Support Vector Machine (SVM), also known as Support Vector Classification, is a supervised and linear ML algorithm used to solve classification problems. The Support Vector Regression (SVR) algorithm is a subset of SVM algorithms that uses the same ideas to tackle regression problems. You can learn more about the SVM algorithm from the Support Vector Machine using Python article.
Here we will use the sklearn module to import the built-in SVM algorithm and apply it to our dataset:
# importing module from sklearn.svm import SVC # initializing SVM SVC_classifier = SVC(kernel = 'linear',gamma = 'scale', shrinking = False,) SVC_classifier.fit(X_train,y_train )
The SVM takes the following parameters:
- kernel : This parameter specifies the type of kernel to be used in the algorithm. We can choose from the
precomputed. The default value of kernel is
- gamma: It is the kernel coefficient for
- shrinking : This parameter represents that whether we want to use shrinking heuristic or not, Default value is
- probability: This parameter enables or disables probability estimates. The default value is
- max_iter: parameter represents the maximum number of iterations within the solver. Its default value is
-1which means there is no limit on the number of iterations.
- random_state: This parameter represents the seed of the pseudo random number generated which is used while shuffling the data.
Once the training is complete, we can predict the outputs by providing only the inputs.
# model predictions SVM_predictions = SVC_classifier.predict(X_test) # printing prodictions print(SVM_predictions)
K-nearest neighbors (KNN)
KNN is a supervised, non-parametric, and lazy learning algorithm mainly used to handle classification problems. It is a lazy learner because it doesn’t learn a discriminative function from the training data but memorizes the training dataset instead. There is no trained model for KNN. Each time we run the algorithm, it processes the entire model to provide the output. You can learn more about the KNN algorithm from the Implementing KNN Algorithm using Python article.
Here we will use the KNN algorithm from the sklearn module and apply it to our dataset:
# importing KNN algorithm from sklearn.neighbors import KNeighborsClassifier # K value set to be 5 KNN_classifer = KNeighborsClassifier(n_neighbors=5) # fitting the model on training dataset KNN_classifer.fit(X_train,y_train)
Once the training is complete, we can provide the test dataset to predict the outputs.
# predicting KNN_predictions = KNN_classifer.predict(X_test) # print print(KNN_predictions)
Decision Tree algorithm
A Decision Tree is a Supervised ML algorithm used to categorize or predict outcomes based on the previous dataset. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules, and each leaf node represents the decision outcome. You can read more about the Decision Tree algorithm from the Implementing Decision Tree Using Python article.
# importing decision tree from sklearn import tree # initializaing the decision tree classifier DT_classifier = tree.DecisionTreeClassifier() # training the tree DT_classifier.fit(X_train,y_train)
The decision tree classifier takes the following parameters:
- criterion: represents the function to measure the quality of a split. You can choose
entropy. By default the value is
- splitter: tells the model, which strategy (
random) to choose while splitting the data. By default the value is
- max_depth: this parameter defines the maximum depth of the tree. The default value is
Nonewhich means the nodes will expand until all leaves are pure or until all leaves contain less than
- min_samples_split: parameter provides the minimum number of samples required to split an internal node. The default value is
- min_samples_leaf: parameter provides the minimum number of samples required to be at a leaf node. The default value is
- min_weight_fraction_leaf: is used to get the minimum weighted fraction of the sum of weights required to be at a leaf node. The default value is
- max_features: defines the number of features of the model to be considered when looking for the best split.
- max_leaf_nodes: this parameter will limit a tree growth to the maximum leaf nodes.
- min_impurity_decrease: this value works as a criterion for a node to split.
- min_impurity_split: represents the threshold for early stopping in tree growth.
Once the training is complete, we can predict the output by providing the testing data.
# predicting outputs DT_predictions = DT_classifier.predict(X_test) # printing print(DT_predictions)
The Decision Tree algorithm can also be used for regression problems:
# regression problem using decision tree DT_regressor = tree.DecisionTreeRegressor() # training DT_regressor.fit(X_train, y_train) # predicting DTR_predictions = DT_regressor.predict(X_test) # printing print(DTR_predictions)
Notice that the output values are not integer values anymore.
Random Forest algorithm
The concept of the Random Forest Algorithm is based on ensemble learning. Ensemble learning is a general meta approach in Machine Learning that seeks better predictive performance by combining the predictions from multiple models. In simple words, It involves fitting many different model types on the same data and using another model to learn the best way to combine the predictions. So, Random Forest Algorithm combines predictions from decision trees and selects the best prediction among those trees. You can learn more about the working of Random Forest Tree from the Implementation of Random Forests algorithm using Python article.
Like the Decision Tree algorithm, we can do classification and regression using the Random Forest Tree. Let’s import the Random forest model from the sklearn module and train it on our dataset:
# importing random forest from sklearn.ensemble import RandomForestClassifier # training the model RF_classifier = RandomForestClassifier(n_estimators = 50) RF_classifier.fit(X_train, y_train)
Once the training is complete, we can provide the testing data to test our algorithm:
# predictions RF_predictions = RF_classifier.predict(X_test) # printing RF_predictions
Similarly, we can apply the Random Forest algorithm for regression problems as well.
# importing random forest from sklearn.ensemble import RandomForestRegressor # training the model RF_regressor = RandomForestRegressor(n_estimators = 50) RF_regressor.fit(X_train, y_train) #predictions RFR_predictions = RF_regressor.predict(X_test) print(RFR_predictions)
Notice that we get continuous values as an output.
Naive Bayes algorithm
The Naive Bayes classification algorithm is a probabilistic classifier, and it belongs to Supervised learning. It is based on probability models that incorporate strong independence assumptions. The independence assumptions often do not have an impact on reality. Therefore they are considered naive. You can read more about the working of Naive Bayes classification from the Implementing Naive Bayes Classification using Python article.
There are several Naive Bayes algorithm implementations (classifiers) available in the sklearn module:
- Gaussian Naive Bayes: assumes that the data from each label is drawn from a simple Gaussian distribution.
- Multinomial Naive Bayes: assumes that the features are drawn from a simple Multinomial distribution.
- Bernouli Naive Bayes: The assumption in this model is that the features are binary (0 and 1).
- Complement Naive Bayes: was designed to correct the severe assumptions made by Multinomial Bayes classifier. This kind of classifier is suitable for imbalanced data sets.
We will use the Gaussian Naive Bayes classifier to classify the testing data here. Let’s train our model using the training dataset:
# import Gaussian Naive Bayes classifier from sklearn.naive_bayes import GaussianNB # create a Gaussian Classifier NB_classifier = GaussianNB() # training the model NB_classifier.fit(X_train, y_train) # testing the model NB_predictions = NB_classifier.predict(X_test) # printing print(NB_predictions)
Boosting methods incrementally build ensemble models. The main principle is to build the model incrementally by training each base model estimator sequentially. AdaBoost is one of the most successful boosting ensemble methods whose main key is in the way they give weights to the instances in the dataset. The algorithm needs to pay less attention to the instances while constructing subsequent models.
Let’s apply this classifier to our dataset:
# Importing the module from sklearn.ensemble import AdaBoostClassifier # adaBoost AB_classifier = AdaBoostClassifier(n_estimators = 50) # training the model AB_classifier.fit(X_train, y_train)
Once the training is complete, we can provide the testing data to predict the outputs.
# predicting the outputs AB_predictions = AB_classifier.predict(X_test) # printing print(AB_predictions)
Similarly, we can apply the AdaBoost algorithm to regression problems:
# importing the module from sklearn.ensemble import AdaBoostRegressor # initializing the algorithm AB_regressor = AdaBoostRegressor() # training the model AB_regressor.fit(X_train, y_train) # prediting ABG_predictions = AB_regressor.predict(X_test) # print print(ABG_predictions)
Gradient Tree Boosting algorithm
The Gradient Boosted Regression Trees (GRBT) algorithm is used for classification and regression problems. It is a generalization of boosting to arbitrary differentiable loss functions.
For creating a Gradient Tree Boost classifier, we need to import GradientBoostingClassifier from the scikit-learn module:
# Importing the GBC from sklearn.ensemble import GradientBoostingClassifier # initializing the model GB_classifier = GradientBoostingClassifier(n_estimators = 70) # training the modle GB_classifier.fit(X_train, y_train) # prediciting GBC_predictions = GB_classifier.predict(X_test) # printing print(GBC_predictions)
For creating a regressor with the Gradient Tree Boost method, we have to import GradientBoostingRegressor from the sklearn module:
# importing the module from sklearn.ensemble import GradientBoostingRegressor # initializing the model GB_regressor = GradientBoostingRegressor() # training the modle GB_regressor.fit(X_train, y_train) # prediciting GBR_predictions = GB_regressor.predict(X_test) # printing print(GBR_predictions)
Unsupervised Machine Learning algorithms
Unsupervised Machine Learning is a technique that teaches machines to classify unlabeled or unclassified data. The idea here is to expose computers to large volumes of data to find common data items’ features and use them to categorize that data. You can learn more about Unsupervised Machine Learning from the Introduction to Unsupervised Machine Learning article.
We will use the digits dataset set from the sklearn module to demonstrate Unsupervised Learning examples. Let’s import the dataset:
# importing datast from sklearn module from sklearn.datasets import load_digits # loading dataset digits = load_digits() #shape of the dataset digits.data.shape
K-means clustering algorithm
The K-means clustering is the Unsupervised Learning algorithm that splits a dataset into K non-overlapping subgroups (clusters). It allows us to split the data into different groups or categories. You can learn more about the working of K-means clustering from the Implementation of K-means clustering algorithm using Python article.
Here we will just import the clustering model from the sklearn module:
# importing the k-mean clustering from sklearn.cluster import KMeans # making 10 clusters kmeans = KMeans(n_clusters = 10, random_state = 0) Kmeans_clusters = kmeans.fit_predict(digits.data)
We can check the number of clusters by printing the shape of the
Notice the shape is now 10, which means it has created 10 clusters as defined by us in the training part.
Hierarchical clustering algorithm
Hierarchical clustering is another Unsupervised Machine Learning algorithm used to group the unlabeled datasets into a cluster. It develops the hierarchy of clusters in the form of a tree-shaped structure known as a dendrogram. You can learn more about hierarchical clustering from the Implementation of Hierarchical clustering using Python article.
Let’s first import the Hierarchical clustering algorithm from sklearn and then apply it to our dataset:
# Importing the algorithm from sklearn.cluster import AgglomerativeClustering # calling the agglomerative algorithm H_clustering = AgglomerativeClustering(n_clusters = 8, affinity = 'euclidean', linkage ='average') # training the model on dataset H_model = H_clustering.fit_predict(digits.data)
We know that Hierarchical clustering works based on the dendrogram. A dendrogram is a tree-like structure that stores each step of the Hierarchical algorithm execution process. In the Dendrogram plot, the x-axis shows all data points, and the y-axis shows the distance between them. We can also visualize the dendrogram using sklearn module.
# importing the required modules import matplotlib.pyplot as plt import scipy.cluster.hierarchy as sch # graph size plt.figure(1, figsize = (16 ,8)) # creating the dendrogram dendrogram = sch.dendrogram(sch.linkage(digits.data, method = "ward")) # ploting graphabs plt.title('Dendrogram') plt.show()
Dimensional reduction using Principal Component Analysis (PCA)
Principal Component Analysis (PCA) is used for linear dimensionality reduction using Singular Value Decomposition (SVD) of the data to project it to a lower-dimensional space. You can learn more about PCA from the Implementing Principal Component Analysis (PCA) using Python article.
Here we will use the PCA to decompose our dataset into 3 and 2 dimensions:
# importing the required PCA from sklearn.decomposition import PCA # PCA with 3 component pca = PCA(n_components = 3) # fitting the dataset fit = pca.fit(digits.data) # printing the three PCA components print(("Explained Variance: %s") % (fit.explained_variance_ratio_))
To have two PCA components, we have to change the number of components to 2.
# PCA with 2 component pca = PCA(n_components = 2) # fitting the dataset fit = pca.fit(digits.data) # printing the three PCA components print(("Explained Variance: %s") % (fit.explained_variance_ratio_))
Once we have decomposed our dataset into 2 and 3 PCA components, we can easily visualize the dataset to see the relationship.
ML model evaluation methods
Model evaluation is essential to assess the efficacy of a model during the initial research phases, and it also plays a role in model monitoring. The model evaluation uses different metrics to understand an ML model’s accuracy and performance. Depending on the model type, there are various kinds of evaluating matrics. In this section, we will use some of these matrices to assess the performance of the models that we had built in the above section.
Evaluation of classification models
A confusion matrix is one of the available methods for calculating and evaluating any classification algorithm’s performance. It helps us calculate the accuracy, precision, f1-score, and recall of the classification model.
Let’s calculate the confusion matrix for the KNN and Decision Tree classifier built in the above sections.
# Making the Confusion Matrix from sklearn.metrics import confusion_matrix # providing actual and predicted values of knn cm_KNN = confusion_matrix(y_test, KNN_predictions) # confusion matrix for decision tree cm_DT= confusion_matrix(y_test, DT_predictions) # printing print("Confusion matrix for KNN: \n", cm_KNN) print("\nConfusion matrix for DT: \n", cm_DT)
Sklearn module also helps us calculate the precision, accuracy, f-score, and recall:
# Importing classification report from sklearn.metrics import classification_report # printing print("classification report for KNN:\n", classification_report(y_test, KNN_predictions)) print("\nclassification report for DT:\n", classification_report(y_test, DT_predictions))
Evaluation of regression models
Different methods are available in the sklearn module to evaluate the regression model. We will evaluate the Decision Tree and Random Forest regression models created in the above sections.
First, let’s find the mean absolute error using the sklearn module:
# Importing mean_absolute_error from sklearn module from sklearn.metrics import mean_absolute_error # printing the mean absolute error print("Means absolute error of Decision Tree is :",mean_absolute_error(y_test, DTR_predictions)) print("Means absolute error of Random forest is :",mean_absolute_error(y_test, RFR_predictions))
Let’s apply these matrices to our model as well:
# Importing mean_squared_error from sklearn module from sklearn.metrics import mean_squared_error import math # printing the mean squared error print("Mean square error of Decision tree is :",mean_squared_error(y_test, DTR_predictions)) print("Mean square error of Random forest is :",mean_squared_error(y_test, RFR_predictions)) print("\nRoot Mean square error of Decision tree is :",math.sqrt(mean_squared_error(y_test, DTR_predictions))) print("Root Mean square error of Random forest is :",math.sqrt(mean_squared_error(y_test, RFR_predictions)))
Cross-validation is a technique for evaluating ML models by training several ML models on subsets of the available input data and evaluating them on the complementary subset of the data. It can be handy to detect the overfitting of the model.
Computing cross-validation in sklearn
The simplest way to use cross-validation is to use cross_val_score from the sklearn module. In the following example, we will see how to estimate the accuracy of various models by fitting the models and computing the score 6 consecutive times:
# importing the cross validation from sklearn from sklearn.model_selection import cross_val_score from sklearn import datasets # loading the iris dataset Input, Target = datasets.load_iris(return_X_y=True) # initializing the models RF_classifier = RandomForestClassifier(n_estimators = 50) NB_classifier = GaussianNB() KNN_classifer = KNeighborsClassifier(n_neighbors=5) DT_classifier = tree.DecisionTreeClassifier() # calculating the scores for each model using cross validation score1 = cross_val_score(RF_classifier, Input, Target, cv=6) score2 = cross_val_score(NB_classifier, Input, Target, cv=6) score3 = cross_val_score(KNN_classifer, Input, Target, cv=6) score4 = cross_val_score(DT_classifier, Input, Target, cv=6) # printing print("Random forest : ", score1) print("NB classifier : ", score2) print("KNN classifier: ", score3) print("Decision Tree : ", score4)
cross_val_score uses the KFold split strategy by default. We can change the cross-validation strategies by passing a cross-validation iterator. For example, see the example below:
# importing the module from sklearn.model_selection import ShuffleSplit # creating iterator cv = ShuffleSplit(n_splits=6, test_size=0.3, random_state=0) # calculating the scores for each model using cross validation score1 = cross_val_score(RF_classifier, Input, Target, cv=cv) score2 = cross_val_score(NB_classifier, Input, Target, cv=cv) score3 = cross_val_score(KNN_classifer, Input, Target, cv=cv) score4 = cross_val_score(DT_classifier, Input, Target, cv=cv) # printing print("Random forest : ", score1) print("NB classifier : ", score2) print("KNN classifier: ", score3) print("Decision Tree : ", score4)
We assume that the data is independent and identically distributed when using cross-validation iteration. Downbelow, we will apply various cross-validation methods to iterate data and split it into the training and testing parts.
Let’s say we have the following dataset.
# importing the module import numpy as np # creating dataset data = np.array([[1, 2], [3, 4], [5, 6], [7, 8],[9,10],[9,4],[6,3],[2,1],[0,9]]) # printing print(data)
K-fold divides all the samples into groups of
k samples known as folds. The following is the pictorial representation of K-fold cross-validation.
Let’s use the sklearn module to implement the K-fold cross-validation on the dataset:
# Importing the kfold module from sklearn.model_selection import KFold # initializing the k-fold k_fold = KFold(n_splits=6) # iterating over dataset for train, test in kf.split(data): print("Training: %s, Testing: %s" % (train, test))
Notice that each time it is assigning different data for the testing part.
Shuffle split iterator
The ShuffleSplit iterator will generate a user-defined number of independent train/test dataset splits. Samples are first shuffled and then split into a pair of train and test sets:
Let’s use the ShuffleSplit iterator using the sklearn module on the dataset:
# Importing shuffle split iterator from sklearn.model_selection import ShuffleSplit # initializing shuffle split iterator shuffle_split = ShuffleSplit(n_splits=4, test_size = 0.2) # iterating over dataset for train, test in ss.split(data): print("Training: %s, Testing: %s" % (train, test))
Scikit-learn is one of the most useful Python libraries for Machine Learning. In this article, we’ve covered the sklearn library, which contains many efficient ML and statistical modeling algorithms, including classification, regression, clustering, and dimensionality reduction.
This is Bashir Alam, majoring in Computer Science and having extensive knowledge of Python, Machine learning, and Data Science. I have been working with different organizations and companies along with my studies. I have solid knowledge and experience of working offline and online, in fact, I am more comfortable in working online.
I love to learn new technologies and skills and I believe I am smart enough to learn new technologies in a short period of time.