The SciPy API provides a 'curve_fit' function in its optimization library to fit the data with a given function. This method applies non-linear least squares to fit the data and extract the optimal parameters out of it.
In this tutorial, we'll learn how to fit the curve with the curve_fit() function by using various fitting functions in Python.
We'll start by loading the required libraries.
from numpy import array, exp
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
Applying the Stochastic
Gradient Descent (SGD) method to the linear classifier or regressor provides the efficient
estimator for classification and regression problems.
Scikit-learn API provides the SGDRegressor class to implement SGD method for regression problems. The SGD regressor applies regularized linear model with SGD learning to build an estimator. A regularizer is a penalty (L1, L2, or Elastic Net) added to the loss function to shrink the model parameters. The SGD regressor works well with large-scale datasets.
In previous post, we learned how to classify data with SGD classifier in Python and you can find it here.
The SciPy API provides a 'leastsq()' function in its optimization library to implement the least-square method to fit the curve data with a given function. The leastsq() function applies the least-square minimization to fit the data.
In this tutorial, we'll learn how to fit the data with the leastsq() function by using various fitting function functions in Python.
We'll start by loading the required libraries.
from numpy import array
from scipy.optimize import leastsq
import matplotlib.pyplot as plt
Applying the Stochastic Gradient Descent (SGD) to the regularized linear methods can help building an estimator for classification and regression problems.
Scikit-learn API provides the SGDClassifier class to implement SGD method for classification problems. The SGDClassifier applies regularized linear model with SGD learning to build an estimator. The SGD classifier works well with large-scale datasets and it is an efficient and easy to implement method.
In this tutorial, we'll briefly learn how to classify data by using the SGDClassifier class in Python. The tutorial
covers:
from sklearn.linear_model import SGDClassifier from sklearn.datasets import load_iris from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report from sklearn.preprocessing import scale
RadiusNeighborsClassifier is a type of nearest-neighbor classification method and it implements radius-based neighbor classification that learning is based the number of neighbors within a fixed radius.
Nearest-neighbor classification is an instance-based learning method. In this type of learning the algorithm compares the test data with the instances stored in the memory.
from sklearn.neighbors import RadiusNeighborsClassifier
from sklearn.datasets import load_iris
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from sklearn.metrics import roc_auc_score
The nearest centroid is simple classifier algorithm that represents each class by its centroid value. The algorithm does not accept any parameter to set. The Scikit-learn API provides the NearestCentroid class for this algorithm.
from sklearn.svm import NearestCentroid from sklearn.datasets import load_iris
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report