Scikit-learn Vs Tensorflow – Detailed Comparison

In this Python tutorial, we will learn about scikit-learn vs Tensorflow and also cover different examples related to scikit-learn and TensorFlow in Python. And, we will cover these topics.

• How scikit-learn works
• How TensorFlow works
• Scikit-learn Vs Tensorflow

How scikit-learn works

In this section, we will learn about the working of scikit-learn by using its scikit-library in python.

• Scikit learn is a user-friendly, open-source, and free library for python.
• The user can use this library freely and that can simplify the task of coding applying in python from this it helps the corder.
• The scikit learn is written in python and built upon Scipy, Numpy, and Matplotlib. It just focuses on modeling the data not to focused on loading or manipulating the data.

Installation of scikit learn

pip install scikit-learn

Syntax:

from sklearn import datasets

Examples of scikit-learn:

In this example, we will learn about how scikit learn library work and how it focuses on the modeling data.

• plt.figure(figsize=(16, 16)) is used to plot the figure on the screen.
• X, y = make_blobs(n_samples=n_samples, random_state=random_state) issued to make blobs that can store binary data.
• y_pred = KMeans(n_clusters=4, random_state=random_state).fit_predict(X) is used to predic incorrect number of cluster.
• plt.subplot(224) is used for plotting the subplots.
• X_varied, y_varied = make_blobs( n_samples=n_samples, cluster_std=[1.0, 2.5, 0.5], random_state=random_state ) is used make different variance.
• X_filtered = np.vstack((X[y == 0][:500], X[y == 1][:100], X[y == 2][:10])) is used make unevenly sized blocks.

import numpy as np
import matplotlib.pyplot as plt

from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs

plt.figure(figsize=(16, 16))

n_samples = 1800
random_state = 200
X, y = make_blobs(n_samples=n_samples, random_state=random_state)


y_pred = KMeans(n_clusters=4, random_state=random_state).fit_predict(X)

plt.subplot(224)
plt.scatter(X[:, 0], X[:, 1], c=y_pred)
plt.title(" Total incorrect number of blobs")


transformations = [[0.60834550, -0.63667344], [-0.40887720, 0.85253230]]
X_aniso = np.dot(X, transformation)
y_pred = KMeans(n_clusters=5, random_state=random_state).fit_predict(X_aniso)

plt.subplot(222)
plt.scatter(X_aniso[:, 0], X_aniso[:, 1], c=y_pred)
plt.title("Total nnumber of Anisotropicly Distributed Blobs")


X_varied, y_varied = make_blobs(
    n_samples=n_samples, cluster_std=[1.0, 2.5, 0.5], random_state=random_state
)
y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_varied)

plt.subplot(223)
plt.scatter(X_varied[:, 0], X_varied[:, 1], c=y_pred)
plt.title("Total number of Unequal Variance")


X_filtered = np.vstack((X[y == 0][:500], X[y == 1][:100], X[y == 2][:10]))
y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_filtered)

plt.subplot(224)
plt.scatter(X_filtered[:, 0], X_filtered[:, 1], c=y_pred)
plt.title("Total number of Unevenly Sized Blobs")

plt.show()

Output:

After running the above code, we get the following output in which we can see that the different K-Mean clustering is drawn on the screen with the help of the scikit-learn library.

Also, check: Scikit-learn logistic regression

How Tenserflow works

In this section, we will learn about the working of Tensorflow by using its TensorFlow library in python.

• TensorFlow is a library that was designed by the Google team which make the works easier for the corder.
• It can calculate the mathematical expression easily and simply.
• Tensorflow involves programming support of deep learning and machine learning techniques.
• Tensorflow can train the deep neural network for handwritten digit classification and create various sequence models.
• Tensorflow has a unique feature of enhancing the same memory and data use.

Installation of Tensorflow:

pip install tensorflow

Syntax to import Tensorflow:

import tensorflow as tf

Example of Tensorflow:

In the following example, we will learn about the accuracy of the model through TensorFlow.

• As we know it combines the algebra of optimization technique makes the calculation easy of any mathematical calculation.
• models = tf.keras.models.Sequential() is used to check the accuracy of model.
• models.compile() is used for compilation of model.
• models.fit(x_train, y_train, epochs=8) is used to fit the model.
• models.evaluate(x_test, y_test) is used to evaluate the whole model.

import tensorflow as tf
mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 260.0, x_test / 260.0

models = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation='softmax')
])

models.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

models.fit(x_train, y_train, epochs=8)
models.evaluate(x_test, y_test)

Output:

After running the above code we get the following output in which we can see that through TensorFlow we can calculate mathematical calculations easier and also get accurate data.

Also, read: Scikit learn Decision Tree

Scikit-learn Vs Tensorflow

Scikit-learn	Tensorflow
1. scikit-learn is used to design to help developers and also used for creating and benchmarking the new model.	1. Tensorflow is also used to design for helping the developers and also used for creating benchmarking the new model.
2. scikit-learn is used in practice with a broad scope of the model.	2. Tensorflow indirect use for the neural network.
3. scikit-learn appliance all of its algorithm as a base estimator	3. Tensorflow appliance all its algorithms in the base class.
4. scikit-learn is more flexible with other frameworks like XGBoost	4. Tensorflow is optimized with a neural network.
5. scikit-learn does not implement a barebone neural network model	5. Tensorflow implements a barebone neural network model.
6. scikit-learn is a higher-level library that implements the machine learning algorithm.	6. Tensorflow is a low-level library that also implements the machine-level algorithm.
7. scikit-learn makes it useful for comparing entirely different types of machine learning models against each other.	7. It enables its specialization under-the-hood optimization which makes it easier to compare the TensorFlow and neural network models.
8. Scikit-learn is mainly used for the machine learning	8. TensorFlow is mainly used for Deep Learning.
9. Scikit-learn is the 3rd party module but it is less popular than TensorFlow.	9. It is also the 3rd party module but is it more popular.

Also you may like:

• Scikit learn accuracy_score
• Scikit learn Random Forest

So, in this tutorial, we discussed scikit-learn Vs Tensorflow and we have also covered different examples related to its implementation. Here is the list of examples that we have covered.

• How scikit-learn works
• How TensorFlow works
• Scikit-learn Vs Tensorflow