Recursive Feature Elimination (RFE) using sklearn

by Amrita Mitra | Jan 13, 2023 | AI, Machine Learning and Deep Learning, Featured, Machine Learning Using Python, Python Scikit-learn

Recursive Feature Elimination Using Sklearn

  species     island  bill_length_mm  ...  flipper_length_mm  body_mass_g     sex
0  Adelie  Torgersen            39.1  ...              181.0       3750.0    Male
1  Adelie  Torgersen            39.5  ...              186.0       3800.0  Female
2  Adelie  Torgersen            40.3  ...              195.0       3250.0  Female
3  Adelie  Torgersen             NaN  ...                NaN          NaN     NaN
4  Adelie  Torgersen            36.7  ...              193.0       3450.0  Female

[5 rows x 7 columns]
species               0
island                0
bill_length_mm        2
bill_depth_mm         2
flipper_length_mm     2
body_mass_g           2
sex                  11
dtype: int64
bill_length_mm       0
bill_depth_mm        0
flipper_length_mm    0
body_mass_g          0
species              0
dtype: int64
   bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g  species
0           39.10           18.7              181.0       3750.0        0
1           39.50           17.4              186.0       3800.0        0
2           40.30           18.0              195.0       3250.0        0
3           44.45           17.3              197.0       4050.0        0
4           36.70           19.3              193.0       3450.0        0
Selected Features: [0 1 2]
   bill_length_mm  bill_depth_mm  flipper_length_mm  species
0           39.10           18.7              181.0        0
1           39.50           17.4              186.0        0
2           40.30           18.0              195.0        0
3           44.45           17.3              197.0        0
4           36.70           19.3              193.0        0

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by Amrita Mitra | October 3, 2023 | Featured, Linear Algebra | 0 Comments

What is the pseudoinverse of a matrix? We know that if A is a square matrix with full rank, then A-1 is said to be the inverse of A if the following condition holds: $latex AA^{-1}=A^{-1}A=I $ The pseudoinverse or the Moore-Penrose inverse of a matrix is a...

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by Amrita Mitra | October 3, 2023 | Featured, Linear Algebra | 0 Comments

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by Amrita Mitra | October 3, 2023 | Featured, Linear Algebra | 0 Comments

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by Amrita Mitra | October 3, 2023 | Featured, Linear Algebra | 0 Comments

We can use the hstack() function from the numpy module to combine two or more NumPy arrays horizontally. For example, we can use the following Python code to combine three NumPy arrays horizontally. import numpy A = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) B =...

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by Amrita Mitra | October 3, 2023 | Featured, Linear Algebra | 0 Comments

Let’s say we have two or more NumPy arrays. We can combine these NumPy arrays vertically using the vstack() function from the numpy module. For example, we can use the following Python code to combine three NumPy arrays vertically. import numpy A = numpy.array([[1, 2,...

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by Amrita Mitra | October 3, 2023 | Featured, Linear Algebra | 0 Comments

Let A be a square matrix. Let’s say A has k eigenvalues λ1, λ2, ... λk. And the corresponding eigenvectors are X1, X2, ... Xk. $latex X_1=\begin{bmatrix} x_{11} \\ x_{21} \\ x_{31} \\ ... \\ x_{k1} \end{bmatrix} \\ X_2=\begin{bmatrix} x_{12} \\ x_{22} \\ x_{32} \\ ......

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by Amrita Mitra | October 3, 2023 | Featured, Linear Algebra | 0 Comments

In our previous article, we discussed what eigen values and eigenvectors of a square matrix are and how we can calculate the eigenvalues and eigenvectors of a square matrix mathematically. We discussed that if A is a square matrix, then $latex (A- \lambda I) \vec{u}=0...