Difference Between Mape And Rmse -

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South Africa's long, multi-format tour of India is nearing its end with the T20I leg starting in Chennai on Friday. India have been dominant throughout, winning the ODIs 3-0 and the one-off Test by ten wickets on the final day.

Laura Wolvaardt's team has been good in patches and showed resilience every time India threw a punch but have still not been able to win a game on tour. With Chloe Tryon back in the side and Marizanne Kapp likely to take the ball, South Africa will pose a tough challenge for India in the T20Is. Here are a few talking points ahead of the series opener:

The unstoppable Mandhana

"She is in the form of her life. It's a privilege to watch."

Former South Africa captain Dane van Niekerk aptly summed up Smriti Mandhana's performance in Bengaluru, on air. With scores of 117, 136, 90 and 149, the India vice-captain has been phenomenal across formats in this series and has looked assured like never before. Her game awareness has peaked and her calmness under pressure has helped her convert starts to big scores. If she carries her form into T20Is, paying just INR 150 to watch her at the MA Chidambaram Stadium is a steal.

The Wolvaardt-Kapp threat

India's biggest challenge will be to keep Wolvaardt and Kapp quiet. Having missed just one T20I, against Sri Lanka, this year the South Africa captain has notched up 247 runs in five innings, with a hundred and two half-centuries, at a strike rate of 132.08 in 2024. Her control and clarity combined with her ability to take calculated risks were visible in the ODI series as well.

Kapp might bowl for the first time on the tour during the T20I series. After playing as a specialist batter in the ODIs and Test due to a minor back injury and workload management, she is likely to threaten Mandhana and Shafali Verma the new ball. Playing at No. 3, she has been aggressive with the bat as well, slamming 208 runs in six T20Is at a strike rate of 136.84 this year. That includes scores of 75, 60 and 44. In Sune Luus' words, Kapp's game " is so sorted at the moment."

Tryon is back

Tryon has been in and out of action for some time now. The allrounder picked up a groin injury during the WBBL in October 2023 and returned for the tour of Australia in January this year. In the next series at home against Sri Lanka, she played only two T20Is and missed the remaining four white-ball games due to recurring back injury. After missing the ODI and Test leg of this series, she was included in T20Is, a much-needed boost for her side against a strong Indian team at home.

An explosive batter in the middle-order who is also effective with her left-arm fingerspin, Tryon has the know-how of the Indian conditions, having played for Harmanpreet Kaur's Mumbai Indians for two years in the WPL. She is yet to pick up a wicket against India in T20Is and it remains to be seen whether she will make her first strike in Chennai. But importantly, South Africa need her to stay fit when they land in Bangladesh for the World Cup in October.

Radha's road to redemption (and Bangladesh)

On the back of an impressive WPL, Radha Yadav returned to the Indian side against Bangladesh in May after last featuring in an international game in February 2023. With Deepti Sharma leading the spin attack, India had to choose between Shreyanka Patil's offspin, S Asha's legspin and Radha's left-arm orthodox. Harmanpreet picked Radha in all of the five games and she repaid the faith with 10 scalps in 19 overs at an economy of 5.05, finishing as the highest-wicket taker in that series. Deepti was the second best for India with five wickets.

She might not have found her feet in the ODIs against South Africa, struggling with speed and length, but the shortest format is her forte. She has learnt to harness her aggression and has showcased better control and accuracy of late. Nine of her ten wickets in Bangladesh were of right-hand batters. With South Africa stacking themselves with right-handers in their line-up, Radha could be a threat on a spin-friendly Chepauk surface. A strong performance here and at the Asia Cup in Sri Lanka later in the month will bode well for India before the World Cup.

Recent form

India come into the series on the back of a 5-0 sweep of Bangladesh in Bangladesh. This was after an underwhelming outing against Australia at home, which India lost 2-1 last December. South Africa, however, have struggled to get more wins in this format, losing four of the six T20Is played this year against Australia and Sri Lanka, all while batting first.

Pitch and conditions

The MA Chidambaram Stadium hosted nine IPL matches during peak summer this year with an average score of 170. Surprisingly, only 25 of the 99 wickets were taken by spinners.

But in the one-off Test against South Africa, the Indian spinners took 19 of the 20 wickets across four days. South Africa's spinners took three out of five wickets. That apart, two days before the opening T20I, there was late-night rain with gusts of wind at Chepauk. The city is expected to have thunderstorms in the late evening almost every day till the end of the series.

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RMSE Formula
RMSE Formula
BtETy
BtETy
RMSE Vs MAPE
RMSE Vs MAPE
12 Table2 1
12 Table2 1
Presents Both The RMSE And MAPE Values For Each Approach Both Error Metrics Show That.ppm
Presents Both The RMSE And MAPE Values For Each Approach Both Error Metrics Show That.ppm
Figure 7 RMSE And MAPE Plotted For Several Different Powers
Figure 7 RMSE And MAPE Plotted For Several Different Powers
RMSE MAPE And Forecasting Result Of The Models
RMSE MAPE And Forecasting Result Of The Models
MAE MAPE RMSE And EV Of Each Forecasting Model
MAE MAPE RMSE And EV Of Each Forecasting Model
Model Comparison By Using MAPE And RMSE
Model Comparison By Using MAPE And RMSE
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0a42140af26f4c329d26a0921c6e88cf
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Accuracy Compare A RMSE B SI C R D MAPE Q320
Accuracy Compare A RMSE B SI C R D MAPE Q320
Results Of The RMSE And MAPE For The Different Models And TIs
Results Of The RMSE And MAPE For The Different Models And TIs
MAPE And RMSE Comparison According To The Number Of Subnetworks The Values In
MAPE And RMSE Comparison According To The Number Of Subnetworks The Values In
The RMSE And MAPE Rankings Of The Different Models Q640
The RMSE And MAPE Rankings Of The Different Models Q640
RMSE And MAPE For Mechanical Properties Predictions After Feature Screening A Q320
RMSE And MAPE For Mechanical Properties Predictions After Feature Screening A Q320
Values Of MSE RMSE MAE MAPE And R 2 For The Eight Models
Values Of MSE RMSE MAE MAPE And R 2 For The Eight Models
Maps Showing Correlation RMSE And Bias Computed For Monthly Averaged Precipitation Mm
Maps Showing Correlation RMSE And Bias Computed For Monthly Averaged Precipitation Mm
Contour Map Of RMSE Value Estimated From Predictive Model Under Different Bandwidths
Contour Map Of RMSE Value Estimated From Predictive Model Under Different Bandwidths
Training A And Forecasting RMSE B MAE C MAPE E Performances Of
Training A And Forecasting RMSE B MAE C MAPE E Performances Of
RMSE Maps For The MAP Approach Using Different Values Of L At 20 DB SNR In The Synthetic
RMSE Maps For The MAP Approach Using Different Values Of L At 20 DB SNR In The Synthetic
Comparison Of RMSE And MAPE With Different Methods
Comparison Of RMSE And MAPE With Different Methods
One Day Ahead Of Comparison Base On MAPE And RMSE Of All The Models For All Datasets
One Day Ahead Of Comparison Base On MAPE And RMSE Of All The Models For All Datasets
MAPE And RMSE For H 1 To 4 Steps Ahead MAPE Mean Absolute Percentage Error RMSE
MAPE And RMSE For H 1 To 4 Steps Ahead MAPE Mean Absolute Percentage Error RMSE
RMSE And MAPE Values To Assess The Performance Of Models
RMSE And MAPE Values To Assess The Performance Of Models
MAPE And RMSE Comparison For Various Models The Values In Parenthesis Indicate RMSEs
MAPE And RMSE Comparison For Various Models The Values In Parenthesis Indicate RMSEs
The RMSE And MAPE Rankings Of The Different Models
The RMSE And MAPE Rankings Of The Different Models
MAE MAPE RMSE Comparison.ppm
MAE MAPE RMSE Comparison.ppm
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MAPE And RMSE Rankings Of Different Methods
MAPE And RMSE Rankings Of Different Methods
MAPE And RMSE Values Of Three Prediction Models With Proposed Clustering Method A For
MAPE And RMSE Values Of Three Prediction Models With Proposed Clustering Method A For
RMSE MAPE And X 10 On The Testing 2 Dataset For Different Flow Phases Using The
RMSE MAPE And X 10 On The Testing 2 Dataset For Different Flow Phases Using The
MAPE And RMSE Of Different Prediction Method
MAPE And RMSE Of Different Prediction Method