Improve model accuracy for training and prediction

Testscan · June 14, 2022, 4:25pm

Hello everyone,

I am new with tensorflow: I have created a neural network model with 20 inputs 1 output and 256 samples. I got a training accuracy of 75% and prediction 69% (calculated with R2 method). Quite poor accuracy. Do you have any suggestion how to improve the accuracy of the model?

Many thanks in advance,

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense
from tensorflow.python.keras.wrappers.scikit_learn import KerasRegressor
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow as tf
import tensorflow_addons as tfa

importmatrix = np.loadtxt('data.txt')
xmatrix=importmatrix[:,0:-1] 
ymatrix=importmatrix[:,-1] 

x = np.array(xmatrix)
y = np.array(ymatrix)

y=np.reshape(y, (-1,1))

scaler_x = MinMaxScaler()
scaler_y = MinMaxScaler()
xscale=scaler_x.transform(x)
yscale=scaler_y.transform(y)

X_train, X_test, y_train, y_test = train_test_split(xscale, (yscale))
rows    = len(xmatrix)
columns = len(xmatrix[0])

model = Sequential()
model.add(Dense(30, input_dim=columns, kernel_initializer='normal', activation='relu'))
model.add(Dense(10, activation='relu'))
model.add(Dense(5, activation='relu'))
model.add(Dense(1, activation='linear'))
model.summary()
model.compile(loss='mse', optimizer='adam', metrics=['mse','mae'])

earlyStopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)

history = model.fit(X_train, y_train, epochs=2500, batch_size=8,  verbose=1, 
                    validation_split=0.2,callbacks=[earlyStopping])

# R2 for trianing model calculation
x0=X_test
a = 1+np.zeros((X_test.shape[0], X_test.shape[1]))
Xnew = a*x0

#Xnew = scaler_x.transform(Xnew)
ynew = model.predict(Xnew)

ynew  = scaler_y.inverse_transform(ynew)
ytest2= scaler_y.inverse_transform(y_test)
Xnew = scaler_x.inverse_transform(Xnew)

metric = tfa.metrics.r_square.RSquare()
metric.update_state(ytraining, yprediction)
result = metric.result()
print('Predictions:',result.numpy()) 

## Example of ImportMatrix: input=importmatrix[:,0:-1] Ouput 
importmatrix[:,-1]
[2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 111
 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 112.5
 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 93.6
 2 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 61.7
 2 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24.8
 2 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 3 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 102.1
 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 92.3
 4 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 85.5
 3 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 73.4
 3 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 74.5
 4 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 91.8
 4 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 103.5
 3 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 42.4
 3 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 52
 4 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 92.8
 4 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 80.8
 4 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 91.1
 4 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 83.1
 3 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 65
 5 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 112.1
 3 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 21.7
 3 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26.8
 3 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26.7
 4 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 71.3
 4 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 65.2
 4 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55.2
 4 3 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 75.5
 4 3 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 72.5
 4 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 76.3
 3 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 33.5
 5 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 109.6
 5 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100
 5 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 106
 5 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 102.5
 4 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 87.3
 4 3 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 80.8
 4 4 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50.3
 4 3 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 36
 6 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 116.8
 5 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 112.1 
 5 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 105.3
 4 4 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 84
 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 101.3
 1 0 0 0 0 0 0 0 4 1 0 0 0 0 0 0 0 0 0 91.3
 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 83
 1 1 0 0 0 0 0 0 4 1 0 0 0 0 0 0 0 0 0 67.2
 2 0 0 0 0 0 0 0 4 0 1 0 0 0 0 0 0 0 0 92.3
 2 0 0 0 0 0 0 0 3 2 0 0 0 0 0 0 0 0 0 80.6
 1 0 0 0 0 0 0 0 5 1 0 0 0 0 0 0 0 0 0 74.8
 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 38.9
 3 0 0 0 0 0 0 0 3 1 1 0 0 0 0 0 0 0 0 87.7
 3 0 0 0 0 0 0 0 2 3 0 0 0 0 0 0 0 0 0 89.2
 1 2 0 0 0 0 0 0 4 1 0 0 0 0 0 0 0 0 0 31.2
 2 0 1 0 0 0 0 0 4 1 0 0 0 0 0 0 0 0 0 81.1
 2 1 0 0 0 0 0 0 3 2 0 0 0 0 0 0 0 0 0 57.6
 2 0 0 0 0 0 0 0 5 0 1 0 0 0 0 0 0 0 0 87.3
 2 0 0 0 0 0 0 0 4 2 0 0 0 0 0 0 0 0 0 80.9
 2 0 0 0 0 0 0 0 4 2 0 0 0 0 0 0 0 0 0 71.7
 2 0 0 0 0 0 0 0 4 2 0 0 0 0 0 0 0 0 0 67.2
 1 1 0 0 0 0 0 0 5 1 0 0 0 0 0 0 0 0 0 45.6
 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 69.9
 4 0 0 0 0 0 0 0 2 2 1 0 0 0 0 0 0 0 0 96.2
 3 0 0 0 0 0 0 0 4 1 1 0 0 0 0 0 0 0 0 95.7 
 3 0 0 0 0 0 0 0 4 1 1 0 0 0 0 0 0 0 0 81.3
 3 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0 83.4
 3 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0 73
 3 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0 68.5
 1 1 0 0 0 0 0 0 6 1 0 0 0 0 0 0 0 0 0 28
 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 97.3
 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 101.3
 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 97.5
 2 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 100
 1 2 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 90.9
 1 3 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 76.4
 2 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 92.7
 2 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 94
 1 4 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 54.5
 2 3 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 73.4
 2 3 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 89.8
 1 5 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 28.7
 2 4 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 56.3
 2 4 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 72.5
 2 4 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 73.3
 3 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 97.3 
 2 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 102.5
 2 2 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 94.2
 2 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 96
 2 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 95.7
 3 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 97.8
 3 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 97.2
 3 0 1 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 99.3
 2 2 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 98.3
 3 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 101.3
 3 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 111.7
 4 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 97.4
 2 3 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 90.7
 2 2 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 82.2
 2 2 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 86.4
 2 2 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 75.5
 3 2 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 90.4
 3 2 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 92.2
 3 1 1 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 96.8
 3 1 1 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 94.3
 3 1 1 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 97.9
 3 2 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 96.4
 2 2 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 95.6
 3 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 99.3
 3 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 99.2
 3 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 103.5
 3 0 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 98.9
 3 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 104.4
 3 2 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 93.7
 4 1 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 97.5
 4 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 100
 4 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 96
 4 0 0 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 105.3
 3 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 97
 4 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 105.3
 2 4 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 70.2
 2 3 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 63.6
 3 3 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 75.9
 3 2 1 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 71.3
 3 2 1 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 94.4
 3 2 1 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 93.4
 3 2 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 96.3
 4 2 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 93.1
 4 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 95.2
 4 1 0 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 106.7
 4 0 2 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 103.5
 3 2 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 99.5
 4 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 106
 4 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 106
 5 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 103.5
 4 2 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 95.6
 5 0 1 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 96.6
 5 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 102.5
 0 0 0 0 0 0 0 0 3 0 0 2 0 0 0 0 0 0 0 93.3
 1 0 0 0 0 0 0 0 3 0 0 1 1 0 0 0 0 0 0 93.6
 0 0 0 0 0 0 0 0 4 0 0 2 0 0 0 0 0 0 0 83.9
 1 1 0 0 0 0 0 0 3 0 0 1 1 0 0 0 0 0 0 90.3
 1 1 0 0 0 0 0 0 2 1 0 2 0 0 0 0 0 0 0 90.8
 1 0 0 0 0 0 0 0 4 0 0 1 1 0 0 0 0 0 0 89.1
 1 1 0 0 0 0 0 0 4 0 0 1 1 0 0 0 0 0 0 85
 3 0 0 0 0 0 0 0 2 2 1 1 1 0 0 0 0 0 0 83.1
 2 0 0 0 1 0 0 0 3 2 1 0 1 0 0 0 0 0 0 80.6
 2 0 0 0 1 0 1 0 3 1 0 1 1 0 0 0 0 0 0 90.6
 0 0 0 0 2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 90.6
 1 0 0 0 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 61
 1 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 99.1
 0 0 0 0 0 0 0 0 1 0 0 4 0 0 0 0 0 0 0 103.5
 2 0 0 0 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 99.2
 0 2 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 71.1
 2 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 97.1
 0 0 0 0 0 0 0 0 2 0 0 4 0 0 0 0 0 0 0 74.8
 0 0 0 0 0 0 0 0 2 0 0 4 0 0 0 0 0 0 0 75.4
 1 0 0 0 0 0 0 0 2 0 0 3 1 0 0 0 0 0 0 75
 2 2 0 0 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 90.2
 4 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 94.3
 0 0 0 0 0 0 0 0 2 4 0 4 0 0 0 0 0 0 0 107.4
 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 120
 1 0 0 0 0 0 0 0 0 0 0 5 1 0 0 0 0 0 0 120.1
 2 0 0 0 0 0 0 0 0 0 0 4 2 0 0 0 0 0 0  117.5
 2 0 0 0 0 0 0 0 0 0 0 4 2 0 0 0 0 0 0 116.4
 1 1 0 0 0 0 0 0 0 0 0 5 1 0 0 0 0 0 0 107.4
 0 0 0 0 1 1 0 0 0 0 0 5 1 0 0 0 0 0 0 115.5
 3 0 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 105.3
 3 0 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 110.5
 3 0 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 120.3
 1 2 0 0 0 0 0 0 0 0 0 5 1 0 0 0 0 0 0 111
 2 0 1 0 0 0 0 0 0 0 0 5 1 0 0 0 0 0 0 113.1
 2 1 0 0 0 0 0 0 0 0 0 4 2 0 0 0 0 0 0 102.5
 2 1 0 0 0 0 0 0 0 0 0 4 2 0 0 0 0 0 0 112.1
 0 0 0 0 0 0 0 0 3 0 0 4 2 0 0 0 0 0 0 104.4
 1 0 0 0 0 2 0 0 0 0 0 5 1 0 0 0 0 0 0 105.3
 1 0 0 0 0 2 0 0 0 0 0 5 1 0 0 0 0 0 0 104.4
 0 1 0 0 1 1 0 0 0 0 0 5 1 0 0 0 0 0 0 102.5
 1 0 0 0 1 0 1 0 0 0 0 5 1 0 0 0 0 0 0 113.1
 0 0 0 0 0 0 0 0 1 0 0 6 2 0 0 0 0 0 0 113.7
 1 3 0 0 0 0 0 0 0 0 0 5 1 0 0 0 0 0 0 104.4
 2 1 1 0 0 0 0 0 0 0 0 5 1 0 0 0 0 0 0 111.4
 2 1 1 0 0 0 0 0 0 0 0 5 1 0 0 0 0 0 0 106.7
 3 0 0 1 0 0 0 0 0 0 0 5 1 0 0 0 0 0 0 115.5
 2 2 0 0 0 0 0 0 0 0 0 4 2 0 0 0 0 0 0 115.5
 2 2 0 0 0 0 0 0 0 0 0 4 2 0 0 0 0 0 0 106
 3 1 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 104.4
 3 1 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 106
 3 1 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 114.8
 3 1 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 106
 2 2 0 0 0 0 0 0 0 0 0 4 2 0 0 0 0 0 0 103.5
 2 2 0 0 0 0 0 0 0 0 0 4 2 0 0 0 0 0 0 112.1
 3 0 1 0 0 0 0 0 0 0 0 4 2 0 0 0 0 0 0 106
 3 0 1 0 0 0 0 0 0 0 0 4 2 0 0 0 0 0 0 110.5
 4 0 0 0 0 0 0 0 0 0 0 2 4 0 0 0 0 0 0 105.3
 1 1 0 0 1 1 0 0 0 0 0 4 2 0 0 0 0 0 0 98.6
 2 0 0 0 0 1 1 0 0 0 0 5 1 0 0 0 0 0 0 105.3
 0 0 0 0 0 0 0 0 4 0 0 4 2 0 0 0 0 0 0 96.4
 1 4 0 0 0 0 0 0 0 0 0 5 1 0 0 0 0 0 0 89.2
 2 2 1 0 0 0 0 0 0 0 0 5 1 0 0 0 0 0 0 103.5
 3 1 0 1 0 0 0 0 0 0 0 5 1 0 0 0 0 0 0 108
 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 108.7
 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 108.6
 1 2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 104
 2 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 106
 2 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 106.3
 3 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 105
 1 3 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 98
 2 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 105
 2 2 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 101
 2 2 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 98.8
 3 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 111
 1 4 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 85.8
 2 2 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 99.4
 2 2 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 103
 2 1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 93.5
 2 0 0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 102.7
 2 3 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 91.7
 3 1 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 102
 2 3 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 94.1
 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 111
 3 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 108.9
 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 105.7
 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 106.8
 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 105.7
 2 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 101.9
 4 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 99
 2 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 86.6
 1 0 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0 0 1 100
 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 1 0 101.3
 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 1 0 101.3
 2 0 0 0 0 0 0 0 0 0 0 0 0 0

Ramyar_Jahani · June 16, 2022, 6:05am

Improving Model Accuracy

Collect data: Increase the number of training examples.
Feature processing: Add more variables and better feature processing.
Model parameter tuning: Consider alternate values for the training parameters used by your learning algorithm.

Testscan · June 27, 2022, 8:27pm

Thanks for the help.
The dataset is defined and I cannot increase it. So unfortunatelly it is not an option.
Also the number of input variables is defined.
Could you please indicate which parameters to alternate (option3 of your reply)?

Thanks